{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1.层次聚类 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple\n",
      "Collecting sklearn2pmml\n",
      "  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/29/b8/930e50ba0b8fb69e5d0fd06370d7ca84752f255c2f4366042b231f5298c8/sklearn2pmml-0.83.0.tar.gz (6.3 MB)Note: you may need to restart the kernel to use updated packages.\n",
      "\n",
      "Requirement already satisfied: joblib>=0.13.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from sklearn2pmml) (1.1.0)\n",
      "Requirement already satisfied: scikit-learn>=0.18.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from sklearn2pmml) (0.24.2)\n",
      "Collecting sklearn-pandas>=0.0.10\n",
      "  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/30/71/ccd5222f731993dfc1a6d9e766a507f1859bda4930b9548e54c11c876baf/sklearn_pandas-2.2.0-py2.py3-none-any.whl (10 kB)\n",
      "Requirement already satisfied: scipy>=0.19.1 in c:\\programdata\\anaconda3\\lib\\site-packages (from scikit-learn>=0.18.0->sklearn2pmml) (1.7.1)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from scikit-learn>=0.18.0->sklearn2pmml) (2.2.0)\n",
      "Requirement already satisfied: numpy>=1.13.3 in c:\\programdata\\anaconda3\\lib\\site-packages (from scikit-learn>=0.18.0->sklearn2pmml) (1.20.3)\n",
      "Requirement already satisfied: pandas>=1.1.4 in c:\\programdata\\anaconda3\\lib\\site-packages (from sklearn-pandas>=0.0.10->sklearn2pmml) (1.3.4)\n",
      "Requirement already satisfied: python-dateutil>=2.7.3 in c:\\programdata\\anaconda3\\lib\\site-packages (from pandas>=1.1.4->sklearn-pandas>=0.0.10->sklearn2pmml) (2.8.2)\n",
      "Requirement already satisfied: pytz>=2017.3 in c:\\programdata\\anaconda3\\lib\\site-packages (from pandas>=1.1.4->sklearn-pandas>=0.0.10->sklearn2pmml) (2021.3)\n",
      "Requirement already satisfied: six>=1.5 in c:\\programdata\\anaconda3\\lib\\site-packages (from python-dateutil>=2.7.3->pandas>=1.1.4->sklearn-pandas>=0.0.10->sklearn2pmml) (1.16.0)\n",
      "Building wheels for collected packages: sklearn2pmml\n",
      "  Building wheel for sklearn2pmml (setup.py): started\n",
      "  Building wheel for sklearn2pmml (setup.py): finished with status 'done'\n",
      "  Created wheel for sklearn2pmml: filename=sklearn2pmml-0.83.0-py3-none-any.whl size=6267700 sha256=ea92ddb1d86ff223ba28f04d2c4a15b8483e9046e4dbbe8b6d921943298ed577\n",
      "  Stored in directory: c:\\users\\taki\\appdata\\local\\pip\\cache\\wheels\\49\\04\\dc\\41df7791433ad87d310476dd2d4fd0ab875a0f757841a9f1d4\n",
      "Successfully built sklearn2pmml\n",
      "Installing collected packages: sklearn-pandas, sklearn2pmml\n",
      "Successfully installed sklearn-pandas-2.2.0 sklearn2pmml-0.83.0\n"
     ]
    }
   ],
   "source": [
    "pip install sklearn2pmml"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn import preprocessing   #导出数据预处理包\n",
    "import numpy as np    #导入np数据\n",
    "import  matplotlib.pyplot as plt  #导入画图包\n",
    "from scipy.cluster.hierarchy import linkage, dendrogram #导入层次画图包\n",
    "from sklearn2pmml import sklearn2pmml\n",
    "from sklearn2pmml.pipeline import PMMLPipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(100, 2)\n",
      "2\n",
      "Chinese    int64\n",
      "Math       int64\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "#导入数据集\n",
    "iris_data=pd.read_csv('data/Score_train.csv',sep = ',')\n",
    "print(iris_data)          #输出数据集\n",
    "print(iris_data.shape)  # 返回几行几列\n",
    "print(iris_data.ndim)   # 返回维度\n",
    "print(iris_data.dtypes) # 返回各列的类型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Chinese</th>\n",
       "      <th>Math</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>78.0</td>\n",
       "      <td>79.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>14.0</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>55.0</td>\n",
       "      <td>55.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>65.0</td>\n",
       "      <td>67.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>78.0</td>\n",
       "      <td>79.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>89.0</td>\n",
       "      <td>90.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Chinese   Math\n",
       "count    100.0  100.0\n",
       "mean      78.0   79.0\n",
       "std       14.0   13.0\n",
       "min       55.0   55.0\n",
       "25%       65.0   67.0\n",
       "50%       78.0   79.0\n",
       "75%       89.0   90.0\n",
       "max      100.0  100.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(iris_data.describe().round(0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 100 entries, 0 to 99\n",
      "Data columns (total 2 columns):\n",
      " #   Column   Non-Null Count  Dtype\n",
      "---  ------   --------------  -----\n",
      " 0   Chinese  100 non-null    int64\n",
      " 1   Math     100 non-null    int64\n",
      "dtypes: int64(2)\n",
      "memory usage: 1.7 KB\n"
     ]
    }
   ],
   "source": [
    "iris_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Chinese', ylabel='Math'>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "from matplotlib import pyplot as plt\n",
    "iris_data.plot(kind='scatter',x='Chinese',y='Math')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib as mpl\n",
    "%matplotlib inline\n",
    "\n",
    "# 设置字体支持\n",
    "mpl.rcParams[\"font.family\"] = \"SimHei\"\n",
    "mpl.rcParams[\"axes.unicode_minus\"]=False\n",
    "\n",
    "iris_data['Chinese'].head(50).plot(kind = 'bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "iris_data.head(50).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABIQAAAFhCAYAAAABY3GgAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAAsPUlEQVR4nO3dfbRlZX0n+O8jiBVFIiyKF3kR6EBVk4ZuM9UkJpOESIwspIOrl7z1BBliL5IZYzrpRMUkK84kYeIYJ4mrO2ZCJ1aAGHmLMzBFNYmNSTRGMWWClGCV0ihYClKx0NIEUOCZP/a51k1x791nn5c65979+axV69Y99zn7/s4+e++zn+999rNLrTUAAAAA9MdzZl0AAAAAAAeWQAgAAACgZwRCAAAAAD0jEAIAAADoGYEQAAAAQM8cPOsCkuTII4+sJ5100qzLAAAAAFgzPv7xj/99rXX9Uj+bi0DopJNOyrZt22ZdBgAAAMCaUUp5cLmfuWQMAAAAoGcEQgAAAAA9IxACAAAA6BmBEAAAAEDPCIQAAAAAekYgBAAAANAzAiEAAACAnhEIAQAAAPSMQAgAAACgZwRCAAAAAD0jEAIAAADoGYEQAAAAQM8IhAAAAAB65uBZFwAL/viuh3Lr3V+YdRkAwH4u+FfH5d9994mzLgMAmKDWEUKllHeXUh4tpXxyv8ffUErZWUq5t5Ty9kWPv6WUcv/gZ6+cRtGsTbfe/YXc9/DeWZcBACxy38N7/cEGANagYUYI/WGS/5zkuoUHSik/lOSCJGfWWp8spRw1ePz0JJck+c4kL07y30opp9Van5504axNpx97WG78iZfNugwAYODi3/vIrEsAAKagdYRQrfWDSfbs9/D/kuRttdYnB20eHTx+QZIbaq1P1lo/m+T+JGdNsF4AAAAAxjTqpNKnJfn+UspdpZS/LKX868HjxyX5/KJ2uwaPPUsp5cpSyrZSyrbdu3ePWAYAAAAAXY0aCB2c5PAk35PkjUluKqWUJGWJtnWpBdRar6m1bqq1blq/fv2IZQAAAADQ1aiB0K4k76uNjyV5JsmRg8dPWNTu+CRfHK9EAAAAACZp1EDo/03y8iQppZyW5JAkf5/ktiSXlFKeV0o5OcmpST42gToBAAAAmJDWu4yVUt6b5OwkR5ZSdiV5a5J3J3n34Fb030hyea21Jrm3lHJTkvuSPJXk9e4wBgAAADBfWgOhWuuly/zox5Zpf3WSq8cpCgAAAIDpGfWSMQAAAABWKYEQAAAAQM8IhAAAAAB6RiAEAAAA0DMCIQAAAICeEQgBAAAA9IxACAAAAKBnBEIAAAAAPSMQAgAAAOgZgRAAAABAzwiEAAAAAHpGIAQAAADQMwIhAAAAgJ4RCAEAAAD0jEAIAAAAoGcEQgAAAAA9IxACAAAA6BmBEAAAAEDPCIQAAAAAekYgBAAAANAzAiEAAACAnhEIAQAAAPSMQAgAAACgZwRCAAAAAD0jEAIAAADoGYEQAAAAQM8IhAAAAAB6RiAEAAAA0DMCIQAAAICeaQ2ESinvLqU8Wkr55BI/+/lSSi2lHLnosbeUUu4vpewspbxy0gUDAAAAMJ5hRgj9YZJz93+wlHJCklckeWjRY6cnuSTJdw6e865SykETqRQAAACAiWgNhGqtH0yyZ4kf/VaSNyWpix67IMkNtdYna62fTXJ/krMmUSgAAAAAkzHSHEKllB9N8oVa6yf2+9FxST6/6Ptdg8eWWsaVpZRtpZRtu3fvHqUMAAAAAEbQORAqpTw/yS8m+eWlfrzEY3WJx1JrvabWuqnWumn9+vVdywAAAABgRAeP8Jx/luTkJJ8opSTJ8Un+tpRyVpoRQScsant8ki+OWyQAAAAAk9N5hFCtdXut9aha60m11pPShEDfVWt9JMltSS4ppTyvlHJyklOTfGyiFQMAAAAwlmFuO//eJB9JsqGUsquU8rrl2tZa701yU5L7ktyR5PW11qcnVSwAAAAA42u9ZKzWemnLz0/a7/urk1w9XlkAAAAATMtIdxkDAAAAYPUSCAEAAAD0jEAIAAAAoGdGue08ALDIH9/1UG69+wuzLgOm4r6H9yZJLv69j8y4EpiOC/7Vcfl3333irMsAOOCMEAKAMd169xe+1WmGteb0Yw/L6cceNusyYCrue3ivQB/oLSOEAGACTj/2sNz4Ey+bdRkAdGDkG9BnRggBAAAA9IxACAAAAKBnBEIAAAAAPSMQAgAAAOgZgRAAAABAzwiEAAAAAHpGIAQAAADQMwIhAAAAgJ4RCAEAAAD0jEAIAAAAoGcEQgAAAAA9IxACAAAA6BmBEAAAAEDPCIQAAAAAekYgBAAAANAzAiEAAACAnhEIAQAAAPSMQAgAAACgZwRCAAAAAD0jEAIAAADoGYEQAAAAQM8IhAAAAAB6RiAEAAAA0DMCIQAAAICeaQ2ESinvLqU8Wkr55KLHfqOUsqOUck8p5f8ppbxo0c/eUkq5v5Sys5TyyinVDQAAAMCIhhkh9IdJzt3vsfcn+Re11jOTfDrJW5KklHJ6kkuSfOfgOe8qpRw0sWoBAAAAGFtrIFRr/WCSPfs99me11qcG3340yfGD/1+Q5IZa65O11s8muT/JWROsFwAAAIAxTWIOoR9P8l8H/z8uyecX/WzX4LFnKaVcWUrZVkrZtnv37gmUAQAAAMAwxgqESim/mOSpJO9ZeGiJZnWp59Zar6m1bqq1blq/fv04ZQAAAADQwcGjPrGUcnmS85OcU2tdCH12JTlhUbPjk3xx9PIAAAAAmLSRRgiVUs5N8uYkP1pr/cdFP7otySWllOeVUk5OcmqSj41fJgAAAACT0jpCqJTy3iRnJzmylLIryVvT3FXseUneX0pJko/WWn+y1npvKeWmJPeluZTs9bXWp6dVPAAAAADdtQZCtdZLl3j4D1Zof3WSq8cpCgAAAIDpmcRdxgAAAABYRQRCAAAAAD0jEAIAAADoGYEQAAAAQM8IhAAAAAB6RiAEAAAA0DMCIQAAAICeEQgBAAAA9IxACAAAAKBnBEIAAAAAPSMQAgAAAOgZgRAAAABAzwiEAAAAAHpGIAQAAADQMwIhAAAAgJ4RCAEAAAD0jEAIAAAAoGcEQgAAAAA9IxACAAAA6BmBEAAAAEDPCIQAAAAAekYgBAAAANAzAiEAAACAnhEIAQAAAPSMQAgAAACgZwRCAAAAAD0jEAIAAADoGYEQAAAAQM8IhAAAAAB6pjUQKqW8u5TyaCnlk4seO6KU8v5SymcGXw9f9LO3lFLuL6XsLKW8clqFAwAAADCaYUYI/WGSc/d77Kokd9ZaT01y5+D7lFJOT3JJku8cPOddpZSDJlYtAAAAAGNrDYRqrR9Msme/hy9Icu3g/9cmefWix2+otT5Za/1skvuTnDWZUgEAAACYhFHnEDq61vpwkgy+HjV4/Lgkn1/UbtfgsWcppVxZStlWStm2e/fuEcsAAAAAoKtJTypdlnisLtWw1npNrXVTrXXT+vXrJ1wGAAAAAMsZNRD6Uinl2CQZfH108PiuJCcsand8ki+OXh4AAAAAkzZqIHRbkssH/788ya2LHr+klPK8UsrJSU5N8rHxSgQAAABgkg5ua1BKeW+Ss5McWUrZleStSd6W5KZSyuuSPJTkwiSptd5bSrkpyX1Jnkry+lrr01OqHQAAAIARtAZCtdZLl/nROcu0vzrJ1eMUBQAAAMD0THpSaQAAAADmnEAIAAAAoGcEQgAAAAA9IxACAAAA6BmBEAAAAEDPCIQAAAAAekYgBAAAANAzAiEAAACAnhEIAQAAAPSMQAgAAACgZwRCAAAAAD0jEAIAAADoGYEQAAAAQM8IhAAAAAB6RiAEAAAA0DMCIQAAAICeEQgBAAAA9MzBsy4AAAD65rEbb8reLVtmXUbvPXnkDyVJHrzsd2dcCYedf34Ov/iiWZcBvSIQAgCAA2zvli15YseOrNu4cdal9No7//7PZ10CSZ7YsSNJBEJwgAmEAABgBtZt3JiXXH/drMuAmXvwstfOugToJXMIAQAAAPSMQAgAAACgZwRCAAAAAD0jEAIAAADoGYEQAAAAQM8IhAAAAAB6RiAEAAAA0DMCIQAAAICeEQgBAAAA9IxACAAAAKBnxgqESik/W0q5t5TyyVLKe0sp60opR5RS3l9K+czg6+GTKhYAAACA8Y0cCJVSjkvy00k21Vr/RZKDklyS5Kokd9ZaT01y5+B7AAAAAObEuJeMHZzk20opByd5fpIvJrkgybWDn1+b5NVj/g4AAAAAJmjkQKjW+oUk70jyUJKHk3y11vpnSY6utT48aPNwkqOWen4p5cpSyrZSyrbdu3ePWgYAAAAAHY1zydjhaUYDnZzkxUleUEr5sWGfX2u9pta6qda6af369aOWAQAAAEBH41wy9sNJPltr3V1r/WaS9yX53iRfKqUcmySDr4+OXyYAAAAAkzJOIPRQku8ppTy/lFKSnJPkU0luS3L5oM3lSW4dr0QAAAAAJungUZ9Ya72rlHJLkr9N8lSSv0tyTZJDk9xUSnldmtDowkkUCgAAAMBkjBwIJUmt9a1J3rrfw0+mGS0EAAAAwBwa97bzAAAAAKwyAiEAAACAnhEIAQAAAPSMQAgAAACgZwRCAAAAAD0jEAIAAADoGYEQAAAAQM8IhAAAAAB6RiAEAAAA0DMCIQAAAICeEQgBAAAA9IxACAAAAKBnBEIAAAAAPSMQAgAAAOgZgRAAAABAzwiEAAAAAHpGIAQAAADQMwIhAAAAgJ4RCAEAAAD0jEAIAAAAoGcEQgAAAAA9IxACAAAA6BmBEAAAAEDPCIQAAAAAekYgBAAAANAzAiEAAACAnhEIAQAAAPSMQAgAAACgZwRCAAAAAD0zViBUSnlRKeWWUsqOUsqnSikvK6UcUUp5fynlM4Ovh0+qWAAAAADGN+4IoXcmuaPWujHJv0zyqSRXJbmz1npqkjsH3wMAAAAwJ0YOhEophyX5gSR/kCS11m/UWr+S5IIk1w6aXZvk1eOVCAAAAMAkjTNC6JQku5NsLqX8XSnl90spL0hydK314SQZfD1qqSeXUq4spWwrpWzbvXv3GGUAAAAA0MU4gdDBSb4rye/WWl+a5B/S4fKwWus1tdZNtdZN69evH6MMAAAAALoYJxDalWRXrfWuwfe3pAmIvlRKOTZJBl8fHa9EAAAAACZp5ECo1vpIks+XUjYMHjonyX1Jbkty+eCxy5PcOlaFAAAAAEzUwWM+/w1J3lNKOSTJA0muSBMy3VRKeV2Sh5JcOObvAAAAAGCCxgqEaq13J9m0xI/OGWe5AAAAAEzPOHMIAQAAALAKjXvJGACryM2fvjlbH9g66zLWnJ17fjBJcsUd18y4krXlvFPOy4WnufIcAGAaBEIAPbL1ga3ZuWdnNhyxob0xQ3vpS/9y1iWsOTv37EwSgRAwdx678abs3bJl1mWsKU/s2JEkefCy1864krXlsPPPz+EXXzTrMphjAiGAntlwxIZsPnfzrMuAFV1xxxWzLgFgSXu3bMkTO3Zk3caNsy5lzbAuJ28hZBMIsRKBEAAAQAfrNm7MS66/btZlwLKMtmIYJpUGAAAA6BmBEAAAAEDPCIQAAAAAekYgBAAAANAzAiEAAACAnhEIAQAAAPSMQAgAAACgZwRCAAAAAD0jEAIAAADoGYEQAAAAQM8IhAAAAAB6RiAEAAAA0DMCIQAAAICeEQgBAAAA9IxACAAAAKBnBEIAAAAAPSMQAgAAAOgZgRAAAABAzwiEAAAAAHpGIAQAAADQMwIhAAAAgJ4RCAEAAAD0zMGzLgAAAABG8diNN2Xvli2zLmPuPLFjR5LkwcteO+NK5s9h55+fwy++aNZlzAUjhAAAAFiV9m7Z8q3wg33WbdyYdRs3zrqMufPEjh0CxEXGHiFUSjkoybYkX6i1nl9KOSLJjUlOSvK5JBfVWh8b9/cAAADA/tZt3JiXXH/drMtgFTBi6p+axAih/5DkU4u+vyrJnbXWU5PcOfgeAAAAgDkx1gihUsrxSV6V5Ook/3Hw8AVJzh78/9okf5HkzeP8njVn2+Zk+y2zrmL+PHJB83Xzr822jnl0xmuSTVfMugoAAADWiHEvGfvtJG9K8sJFjx1da304SWqtD5dSjhrzd6w9229JHtmeHHPGrCuZKzeeeOusS5hPj2xvvgqEAAAAmJCRA6FSyvlJHq21fryUcvYIz78yyZVJcuKJJ45axup1zBnJFbfPugpWg82vmnUFAAAArDHjzCH0fUl+tJTyuSQ3JHl5KeWPknyplHJskgy+PrrUk2ut19RaN9VaN61fv36MMgAAAADoYuRAqNb6llrr8bXWk5JckuQDtdYfS3JbkssHzS5P4jogAAAAgDkyibuM7e9tSV5RSvlMklcMvgcAAABgTow7qXSSpNb6F2nuJpZa65eTnDOJ5QIAAAAwedMYIQQAAADAHBMIAQAAAPSMQAgAAACgZwRCAAAAAD0jEAIAAADoGYEQAAAAQM8IhAAAAAB6RiAEAAAA0DMCIQAAAICeEQgBAAAA9IxACAAAAKBnBEIAAAAAPXPwrAuAubNtc7L9lllXsc8j9zRfN79qtnUsdsZrkk1XzLoKAAAARmSEEOxv+y3JI9tnXcU+x5zZ/JsXj2yfr8AMAACAzowQgqUcc0Zyxe2zrmI+zdNIJQAAAEYiEAKANebmT9+crQ9snXUZY9mxZ0eS5Io7Vvflqeedcl4uPO3CWZcBAPAsAiGAKZunzvm8dbJ1lqdj6wNbs3PPzmw4YsOsSxnZxiM2zrqEse3cszNJbOMAwFwSCAFM2Tx1zuepk62zPF0bjtiQzedunnUZvTYvwSsAwFIEQgAHgM75s+ksAwCsTY/deFP2btky6zKe5YkdzWj5By977YwrebbDzj8/h1980QH9ne4yBgAAAEzM3i1bvhW+zJN1Gzdm3cb5GTG/4IkdO2YSoBkhBAAAAEzUuo0b85Lrr5t1GavCrEYsGSEEAAAA0DMCIQAAAICeEQgBAAAA9IxACAAAAKBnBEIAAAAAPSMQAgAAAOiZtX/b+W2bk+23zLqKf+qRe5qvm1812zoWO+M1yaYrZl0FAEzMzZ++OVsf2Dqz379jz44kyRV3zObz9bxTzsuFp104k98NAMy/tR8Ibb8leWR7cswZs65kn2POnHUF/9Qj25uvAiEA1pCtD2zNzj07s+GIDTP5/RuP2DiT35skO/fsTJJeB0KP3XhT9m7ZMusylvXEjiYwfPCy1864kuUddv75Ofzii2ZdBgBTsvYDoaQJg664fdZVzK95GqkEABO04YgN2Xzu5lmXccDNalTSPNm7ZUue2LEj6zbOLphbybzWtWAhsBIIAaxdIwdCpZQTklyX5JgkzyS5ptb6zlLKEUluTHJSks8luajW+tj4pQIAwPDWbdyYl1x/3azLWJXmeeQSAJMxzqTSTyX5uVrrP0/yPUleX0o5PclVSe6stZ6a5M7B9wAAAADMiZFHCNVaH07y8OD/XyulfCrJcUkuSHL2oNm1Sf4iyZvHqhIAAKCjacwlNa35n8zZBBxoE7ntfCnlpCQvTXJXkqMHYdFCaHTUMs+5spSyrZSybffu3ZMoAwAA4FsW5pKapHUbN058DqgnduyY60nQgbVp7EmlSymHJvmTJD9Ta91bShnqebXWa5JckySbNm2q49YBAACwv9Uwl9Ram7PpQN7l70Dfsc9ILtaSsUYIlVKemyYMek+t9X2Dh79USjl28PNjkzw6XokAAACsFtMYmbWcaYzYWo6RXKw149xlrCT5gySfqrX+5qIf3Zbk8iRvG3y9dawKAQAAWFVWw8isrtbaSC4Y55Kx70tyWZLtpZS7B4/9Qpog6KZSyuuSPJTkwrEqBAAAAGCixrnL2F8lWW7CoHNGXS4AAAAA0zWRu4wBAAAAsHoIhAAAAAB6RiAEAAAA0DMCIQAAAICeEQgBAAAA9Mw4t50HYA27+dM3Z+sDW6e2/B17diRJrrjjiqn9jvNOOS8Xnnbh1JYPAKwej914U/Zu2TLy85/Y0Zy7PHjZa8eq47Dzz8/hF1801jJgEgRCsFZt25xsv2Xyy33knubr5ldNdrlnvCbZNL1ggO62PrA1O/fszIYjNkxl+RuP2DiV5S7YuWdnkgiEAJiJLuFD16BBoDCavVu25IkdO7Ju42jnIKM+b7GF99r7xzwQCMFatf2W5JHtyTFnTHa5x5w52eUlTZ2JQGgObThiQzafu3nWZYxkmiOPAKBNl/ChS9AgUBjPuo0b85Lrr5vZ7x93dBFMkkAI1rJjzkiuuH3WVbSb9GgjAIA5MI3wQaAATIpACKCjrnPrjDpXjvlvSEaby8k2BwBAG4EQQEdd59YZZa4c89+wYJS5nGxzAACztRrmERMIAYxg2nPrmP+GxQ7EXE62OebJuHcCStwNyN2UAGZrNcwjJhACAGCujHsnoMTdgNxNCWD25n0esdUVCI1yG+1Rb5E977fAnuQtxSd9G/F5X3cAMMdGmTdqf6POI7W/Wc4rNes7ASWrf/LeWa/DA7H+2kZCDTvSyUgmoI9WVyA0ym20R7lF9mq4BfYkbyk+yduIr4Z1BwBzbJR5o/Y3yjxS+zOvFKtB20ioYUY6GclEn03iEt2lTOqy06UIcCdndQVCyYG5jfZquQX2PN5SfLWsOwCYYwdi3qi2kUg1NTv27FhxlJE70zEPxh0JtdpHgsE4JnGJ7lImvbwFAtzJWn2BEAAAY2sbidQ2ysgIIljbxhk5Mu7oECNADqxZX17ahQB3sgRCAMydeZlDxegH1rpxRiK5Mx2sbeOMHBlndIgRIHDgCIQAmDvzMIeK0Q+w9nQd8TDqKAejG1grZjFyxAgQOHAEQgDMpQMxh8pKjH6YP11Hjo06SszIsLWr64iHUUY5GN3AJC0VYq4UVAojgS4EQgDAqtB15Ngoo8SMDFv7pj3iwegGJmmpEHO5oFIYCXQlEALoqbbRFsOOrjCaggNp2iPHjAyD4bVdgjfMJXdGtLQbNsQURs5Ol8tRu16Kah/pt2mPEuxfILRtc7L9lpXbPHJP83WlW6if8Zpkk5NGWNIw+9liw+xzS5nyfrhcYLJcUDJsMDLMZS/DhDHjBjHj3mEoMZoCoM/aLsFru+TOiBamZbmAZrmO9LihS5fLUbtcimofYdqjBPsXCG2/JXlke3LMGcu3OebMlZfxyPbm66Q7ol060aN0oMfpPA9bW5e6hGrjaXtPhn0vpvE+DLOfLda2zy1lWvvhIssFJksFJV2CkWEuezlQt3sed7TFahtN0WUOmlHmn5nX0VKTCiEXzOvrBA68cS7BM6KFaVkuoFmqIz2p0GUal6PaR0imO0pwfgOhpTq7K3Vwu3RqjzkjueL20WvrOophWF060V070ON2noetbdi6DkBnvvMolQWjjlZJDmzI1faeDPNeTPN9GHc/azOt/XA/wwYmXYORvgUxy+k6CisZL4zoMgdN1/ln5nm01CRCyAXz/DrhQJiHy6SGuTzF5Vr0nUvt5l/XOz8mo9390bFuefMbCC3V2V2ug3sgwoUDZdxO9LIhSG2Cjv070QcySFvsQHTmu45SWTDKaJVkNtvhvIabkzCJEVBGoc29LqOwksmEEdOag2beQ7pJve55f51tJjF3Vh9HSC213iYR3B7oyzomYR4ukxrm8hSXawHzruudH5Pud390rFvZ/AZCyfCd3Wl1apfrkE5qpNI0LBeCLBVyrKUgbTlTC9iWskzotpxZbyvzbtwRUH3YvteILkHFag8jVjKtOasmVUdbPfub19Bk3LmzJhFKrsZ1vNR6m0RwO4vLOiZhHi6TGvfyFKMiJsvt4WE0a+HOj6vxjxsL5jsQmrUu4UoyPx3QWQdpydoZ3TGty/jmZVuZd+MEevM8+gmWMK05qyZVx0r17G/eLysbZ7TUJELJ1bqOp3UJ7Vq+rENIsM88rItpdtrcHh76a7X+cSMRCLXr0iHVAd1nLY3umMZcOLYVOpjWpRrMn2l1uKdVx3LW8kiuSbGO+0FIsM88rItpd9rWcrg5LfMQFLK6TWJet2T8bWu17v9TC4RKKecmeWeSg5L8fq31bdP6XavSNCfNnhdGd6zOyw6ZO9O6VIPRzMulXWuZdcy4uo4ESabX0VytnYRpmId1MQ81zIN5mZh8HoLCeTGtcGxeApNp6TKv2zd3787TX/7ys9o887Wv5YkdO561nub1NU/SVAKhUspBSX4nySuS7EryN6WU22qt903j961KfZ00eynzEI51DW6GrWFeLjuc1uub1nJ5llmPHFmNHfRpjayal0u75sG0tot5WcfzMDpvNe5786DLSJBkbXc0V/PcFkxPl4nJu3aik27b0WoK6aYZNk8rHJuHwGTaIX2XbejpL395qImp1/LnwmLTGiF0VpL7a60PJEkp5YYkFyQRCC02D3P9zIN5CMemORn3PFx2OK3XZxLz3piXDnoX0xxZNeuAbl5Mc7uYh3U8D6PzVuO+Ny+6TFQ6Dx3NaVnNc1swXdPoRCdrezuadtg8rXBs1oHJPIX0qymAPBCmFQgdl+Tzi77fleS7p/S7WAvmIRybhxqmaVqvb17W2/7LX2mU2aTnhOqJeeigd7Uaa15t1vo6nofXNw81sLrpADEuAes+a31dzDqUGmXZjK7UWie/0FIuTPLKWuu/H3x/WZKzaq1vWNTmyiRXDr7dkGTnxAsBAAAA6K+X1FrXL/WDaY0Q2pXkhEXfH5/ki4sb1FqvSXLNlH4/AAAAAMt4zpSW+zdJTi2lnFxKOSTJJUlum9LvAgAAAKCDqYwQqrU+VUr5qSR/mua28++utd47jd8FAAAAQDdTmUMIAAAAgPk1rUvGAAAAAJhTAiEAAACAnhEIAQAAAPTMtG47zxpRSrlo8N/vS/LhWutNs6wHDoRSyu8luTXJn9Zan551PdAXpZRLkxyf5HeTvKzW+v4Zl5RSyitrrX86g9/7XWnO016d5FO11usntNx/n+Sswbe31lpvn8RyYZ6VUs4b/Pe7k3y11vqbs6xntSil/FKSryc5NMmTtdbfmHFJTEkp5X9OctTg2y/VWq9doe1lSUqSb0/yeK3196dfIdNihBBtTknyD0m+kuSjsy0FDpj7knw5yS+UUn551sVAj5yaJgx6U5JzZlVEKeW9pZQ3lVLenOR/nVEZP5jklbXWX0hy8gSXe3St9cokDyTZMMHlwjz7l0lOSvLrSV4000pWl39Ickqt9deSPH/WxTBVR9da315rfXuSo1vaHpPk9Frrf0py3PRLY5rmboRQKeXGJB9I8ie11r9vafsbSZ5M8kSS5w9OmpZr+4YkRyT5L2n+6vgnK7T9+SSnpUk9b6u1vmfI2i+std48iRoG7X86ybrBt48Pdrq2to8n+bbBzrxc26HXW631baWU70/ylVrrQyvVu9/vWHFddG3f8f376STPS5Nct623Ltvbm5PU7BsttdI6PjvN+/EDaVL2d7Yse1rb/dB1dNnuu6zj/Z7X9j53Wm7H7eLSJM8kOT3NHRZXCnruqbXeleSuUsqKwXnH92PobWjQfqj10WX/H7TvdCxa9Lxh9tNvG7aODsvt+vp+Lk1Hd5htucv71+XYMvT21nE7PjvD79Ndt7cuNXdZF28e/Pd7h6jjE7XWr5dS/rckv9pS79kZfl102oaS/N+11r8cPPfMljq6HDuHrjnNe/HZwf+3tdQw9Daf5JODoPtPMsR54BTPibpsb1NZx11fW8dlvz3JNzLEsWW/503sONux3qkchwbtu55fDHs++6bBf4c5xt2a5N+mCYRWHO1eSrkhyZ9niHOyru1H+MwZdl102Z+GPudM8mCS+0spV2bf8Wi55U7zM6fLeXKXtl0+y6ZyDjBoP/S23HG76PKe/PWgjmeSfGSlepN8OPsGCnxypYYjrIuhj53T6gd03IamdU7ddX8a+tiyv3kcIfTXSW5Lcmkp5Vda2j6W5g34rbS/lhcm+ZUkr03yIy1tD0vyM0nuzr6hc0sqpWwd/BXxTUl+bIgafnXIGpLmtf1fSX5z8P+VPDfJM4OD0/Na2i6st9/MENtArfVDtdbfamu3aF28Oe3rIqWU/9qhfZd1d3ia1zbMevvrNCcJw2xvDya5K8mdQ3QmzknyfYOd8dCWtl3r6PL+dalj6O1+0e9vXccd95GhlzvQZbv4jjSv64/Sso/UWv980f+faVlul+PQwjb0gSHDkmHXR5f9P2m2haHWW8f375Bh61i0/w+z3K6v70UZflvu8v512T4Xtrfr015zl+24yz7d5ZiVdKu5y7p4MM3JZWsdtdZbB1+fqbX+Ystyu6yLTtvQQhg0+P89Lc0PS/IfMtz2NnTNtdZ31lr/aPD/rS3LfVGG3OZrrbfWWn+l1npvrfUTLctNmk78UMvueB4w9DE53T6fpvW513XZX8mQx5ZpHWc71jv050LH5SYdtqF021cfypCfqbXW+2qtv1Zr/dlaa1tn9yMZvi/StX2Xz5wu66LL/jR0X6vW+r5a6/9Xa72m1npdy3K7nuN0rXnY8+Qufckun2WL2x7e0nbx5+khLW2TfdvyMJ/XXbaLod+TQX/v7bXWd9RaP9TS9q8Xzo+HCEC6nFsk+46dw/RzuvTvp7KPZHrn1F33p079+8XmMRC6IMlLk7yrLUFMclCatPrn0lzesZLPJfn5JL+T5OMtbZ9K8pNp/nL29Za2f5nmDTs+zcazkgeSvCbJi5PsammbJF9Mk/4+nmRPS9sXJHm0lPJTSR5paVsGy747ye4h6hjWh9Ikme9KMsycBFcNDjz/Z5K2k917kpyb5q9i97e0/cEkV6U5Qf/jlraHJbksyeYkf9XS9mtJzkzy5lLKz7a0vS/J3lLKoRluPzssySVpTpq+1tJ24f37uyTbW9ruSDPK5X9P896s5Mk0J2zfkfaav5zkLYOv/9jS9iNpkv5D0r6Ob0/yxiRfTZPit/nwoOP4trT8hSLNfvpMkkuTfGaIZQ/roDTHl3+b5M6WtqcmeVmSF5ZS3jjEst+T5KeSvD4rHwO+LcnnSynvTHOcafPcJD+d5hjXtj99NM0x5fEkW1ra3pXkQ6WU/yPNOlnJL6U5fr4oLaMf0mybnxpsx23bW9LU+ZNJrh2i/Q8n+UKa969tvppn0rwfb0mzD66kJnlnkp1pTvRWsivNicE/pH1dXJzko6WUg9L8Rbqthu9N8utl33xwK3lg8Jy/TfKllrZPJ/mPGW5dHJrm/X5H2TePxyR8JsnnBn+dKy1tX5CkDI7Jk778+RtpzhVOHvy/re3e0szLMclzsDvSHL9PzXAd9C7+Mc0x6Pq0nxMdnOZY/I40n8ErOSHNPvXqJL/X0vZ70ry+65K8r6XtV5I8WUq5Ou3r+JtpLgk8KMONmv9qkv+plHLNEG3/Ism7k/xomvOjlXwgzWfvdyT5s5a2d9Ra3zH4/9+2tN02qGMYV6Q5z3pH2s+TP5lk5+Azp+3cImm2m59J8/raOscvSPLUYB95YqWGtdYbkjya5GullFcOUcew/irN9nlshjtf+FCt9eFBB/3DLW0PTvLf0xw//3tL21cluW9wvG/rYyTN5+mlSdpG/Rya5PvTHC/azp26OCTJP0/yTGnmKmtzf5pg5ZK0n4s8N03H/4w05xorOTRNX/LINMeDlTyd5nzoqrSfLzyd5hz1y2nf7j+d5pz+40n2trRdaP+NJFeVUtqCgieTnDY4DrUdA0qaftHTpZRXDFHHNHwgzbq4J+2fIUlzbvjHabbNe1vaPprm8+OUtG/L16R5385P+3nZtydZWF9tbb+ZZFOavlxb//7DaV7TC5J8sKXty9Ns78Oss6T5vNuYpm/Y9pnzrCfOm1sz/NwdX09yQq31V9J+Xet3pJmX4I1pNpqVPJ7kqFrrr6YJb1ZSBnXsSfLelrYnpTnh35Omo9Jmfa31kCS/nOZAuJLH01zL+Z/TXvMzgxrOTLPBT8pzM/w6TpqD3sJfEts6CWdk37XfL2xpe2uS/5Zmm7i0pe1zsm++ipe3tD0zzYfBP0v7XwYWz4Pxgpa2SbMNDXuN9tNpwqAb0r7Dnzj49+vZd/nhcp5J88H5+rRvF4em2YaOTvv70eW1/Y9JnltrfUHaO3hJ8pOLtqEfbml7dPYdLyZ5vfNz0oSgp6Z9zpOvJ3nxkOsiaU5+1g3+rfT6nhos+ysZ7kPgaxn+PVk4tvynNCfHK/nXSX6kDjfnyQ8satv2Xr8wyUvSbMfDHLN+Js3x6LK0H1sWjhcnpRmWu5LHkxw75GdDrbU+J00Q0vaXqKOzbx23Lfd30pzs/0Lat/mT05yAvSPDhSAnpjkOHJX2OTYWjp3PTXNMXMnRtdYfSfLWNCcrk3J0klMH6+2glrYl05ubqNZaD0oTqLW91wvnLb+WyZ6DnZXkoFrrj2e4Y2cXC5/tV6b9vb49g3O4NH8tXcm9aQKLdWn/S+ntaQL3H09yeUvbZ9JcDvCLaV8XX09yYodj8vNqraekea8fb2n7hjTnFSemWXdty12Y16ltG/qlRZ97bcu9JMn/MGh7Vkvb38m+P/Yc09L25DR/jf5Kmr+2tylJPpGms9R2rjz051Mp5b1J/k2S9ZnsXF8/kG7zd72xDD/n2ONpzu+H+Rz5Vp8o7Z9PXx8sd5g+0dPZ1x+ZZEB+dJKT6vDzypyaZjTDMJ8ji/eRtrnPnsnw80V9PcnJQx4Dvpmms/3bae/HnZzmXP23M9w+8oNJfrjW+uK096G+vcO6WFjHR2V28/KdleaY/PwM9/l0VpIfGhxr2/a/xXPitfWVfzvNe3xI2kcTLT73bduWn86+40Xb63smw5/P3pvkb5K8eIhMJGnORV6e5tK1TvMDzt0cQukwd0c6XNeaDvMSpMN1kXXfHDtfrS1z7HRpu/C7BxvALWnfyIeuOR2uo+6oyzpOOszRkG41d9mG7u5Qc5cauq6LhW35J9KyLddu8zp1qXlabYd+bekwZ8ZAl22oyz7SRZdtqMu6SIZ8fR23ia51LF5vbX+p6fL+dWnb9ZjVZbvocrzosg1N6/g9dL0jbBdd1nOX7b7TnDUddFlvXertqsvr63Le0kXXY2cXXT7PuuxP02rbZV10PSZ3ea+7HIemtdypHAu7Hls6tu+yj3R5fV3My7nIVPpEI/RHhtX1PKvLsaXLPjLKOWqX9da6HY/w+dtlm+uyLrr2R6ah6/40rXUxrf10WuezXfb/ZIxzrVLrMCM9AQAAAFgr5vGSMQAAAACmSCAEAAAA0DMCIQAAAICeEQgBAAAA9Mz/D4UKJQ0R3YbpAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1440x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.cluster.hierarchy import linkage, dendrogram  #导入层次聚类包\n",
    "plt.figure(figsize=(20,6))  #画图层次聚类图\n",
    "#层次聚类，采用离差平方和欧式距离作为类间和类内度量方式\n",
    "Z = linkage(iris_data, method='ward', metric='euclidean') #调用SK提供层次聚类方法\n",
    "p = dendrogram(Z, 0)     #画出层次聚类图\n",
    "plt.show()  #展示层次聚类树"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PMMLPipeline(steps=[('AgglomerativeClustering', AgglomerativeClustering(n_clusters=4))])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.cluster import AgglomerativeClustering\n",
    "ac=PMMLPipeline([(\"AgglomerativeClustering\",AgglomerativeClustering(n_clusters=4, affinity='euclidean', linkage='ward'))])\n",
    "#ac = AgglomerativeClustering(n_clusters=4, affinity='euclidean', linkage='ward')\n",
    "ac.fit(iris_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2 0 0 2 1 0 2 0 2 0 0 1 1 3 2 3 2 2 3 0 0 1 1 0 2 1 2 1 0 0 0 0 0 1 2 0 1\n",
      " 2 2 1 2 1 1 0 0 3 2 2 1 3 1 0 2 0 2 3 2 2 2 0 1 2 1 0 1 1 0 2 1 1 1 0 3 2\n",
      " 2 0 3 1 0 3 2 1 0 3 0 0 2 1 1 3 1 3 2 2 0 3 0 2 2 0]\n"
     ]
    }
   ],
   "source": [
    "labels = ac.fit_predict(iris_data)\n",
    "print(labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(iris_data['Chinese'], iris_data['Math'], c=labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 测试"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      sno  Chinese  Math\n",
      "0   10001       68    78\n",
      "1   10002       83    75\n",
      "2   10003       86    66\n",
      "3   10004       82    64\n",
      "4   10005       56    91\n",
      "5   10006       57    79\n",
      "6   10007       97    89\n",
      "7   10008       82    82\n",
      "8   10009       79    79\n",
      "9   10010       96    86\n",
      "10  10011       82    65\n",
      "11  10012       91    78\n",
      "12  10013       95    55\n",
      "13  10014       58    88\n",
      "14  10015       87    98\n",
      "15  10016       95    76\n",
      "16  10017       59    73\n",
      "17  10018       61    58\n",
      "18  10019       88    65\n",
      "19  10020       89    91\n",
      "20  10021       99    92\n",
      "21  10022       87    95\n",
      "22  10023       91    91\n",
      "23  10024       80    58\n",
      "24  10025       97    59\n",
      "25  10026       65    60\n",
      "26  10027       58    63\n",
      "27  10028       97    60\n",
      "28  10029       81    75\n",
      "29  10030       83    65\n",
      "30  10031       72    99\n",
      "31  10032       81    63\n",
      "32  10033       83    69\n",
      "33  10034      100    59\n",
      "34  10035       84    71\n",
      "35  10036       79    84\n",
      "36  10037       96    99\n",
      "37  10038       72    57\n",
      "38  10039       88    74\n",
      "39  10040       82    99\n",
      "40  10041       58    71\n",
      "41  10042       78    61\n",
      "42  10043       55    83\n",
      "43  10044       65    83\n",
      "44  10045       63    92\n",
      "45  10046       75    83\n",
      "46  10047       65    87\n",
      "47  10048       55    79\n",
      "48  10049       75    77\n",
      "49  10050       76    61\n"
     ]
    }
   ],
   "source": [
    "iris_data2=pd.read_csv('data/Score.csv',sep = ',')\n",
    "print(iris_data2)          #输出数据集\n",
    "del iris_data2['sno']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 3 1 1 0 0 2 3 3 2 1 3 1 0 2 3 0 0 1 2 2 2 2 1 1 0 0 1 3 1 2 1 3 1 3 3 2\n",
      " 1 3 2 0 1 0 0 0 3 0 0 3 1]\n"
     ]
    }
   ],
   "source": [
    "labels2 = ac.fit_predict(iris_data2)\n",
    "print(labels2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(iris_data2['Chinese'], iris_data2['Math'], c=labels2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2.K-Means "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'pd' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_10932/4188216712.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcluster\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mKMeans\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0miris_data\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'data/Score_train.csv'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0msep\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m','\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0miris_data\u001b[0m\u001b[1;33m)\u001b[0m          \u001b[1;31m#输出数据集\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'pd' is not defined"
     ]
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "iris_data=pd.read_csv('data/Score_train.csv',sep = ',')\n",
    "print(iris_data)          #输出数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "km =KMeans(n_clusters=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(n_clusters=4)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#km =PMMLPipeline(([(\"KMeans\",KMeans(n_clusters=4))]))\n",
    "km.fit(iris_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 0 0 1 2 0 1 2 1 0 0 2 2 3 1 3 1 1 3 0 0 2 2 0 1 2 3 2 0 0 0 0 0 2 1 0 2\n",
      " 1 1 2 1 2 2 0 0 3 1 1 2 3 2 0 3 0 1 3 1 1 1 0 2 3 2 0 2 2 0 2 2 2 2 0 3 1\n",
      " 1 3 3 2 0 3 1 2 0 3 0 0 1 2 2 3 2 3 1 1 0 3 0 0 3 0]\n"
     ]
    }
   ],
   "source": [
    "labels = km.fit_predict(iris_data)\n",
    "print(labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD4CAYAAAAXUaZHAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAABL7UlEQVR4nO3dd5hTVfrA8e+56Zle6L333kERFEUBe18LVlzXVVdd2293bau7tlV31bUXdHftBbsCAgoKiPQqdYBhmBkYpqbfe35/ZECGZCiZTJJJzud5eMLcTO59cyd5c3LuOe8RUkoURVGU5KLFOwBFURQl+lRyVxRFSUIquSuKoiQhldwVRVGSkEruiqIoScgc7wAA8vPzZceOHeMdhqIoSpPy888/75FSNgt3X0Ik944dO7JkyZJ4h6EoitKkCCEK6rtPdcsoiqIkIZXcFUVRkpBK7oqiKElIJXdFUZQkpJK7oqS4grU7WD1/HR6XN96hKFF0xNEyQohXgSlAiZSyb+22XOAdoCOwDbhASrmv9r67gasBHbhJSvl1o0SuKEqDlGwv5U9T/s7uLSVoZg09YHD9k1OZfO3J8Q5NiYKjabm/Dpx6yLa7gNlSym7A7NqfEUL0Bi4C+tQ+5t9CCFPUolUUJSqklNx92t/Yvq4Qj8uLq9KN1+XluVums+aHDfEOT4mCIyZ3KeV3QNkhm88Eptf+fzpw1kHb35ZSeqWUW4FNwPDohKooSrRsXrGNku2lGLpRZ7vP7eWjp7+IU1RKNEXa595CSlkEUHvbvHZ7G2DHQb+3s3ZbCCHENCHEEiHEktLS0gjDUBQlEpV7qzGZQr9USwllRfviEJESbdGeoSrCbAu7GoiU8kXgRYChQ4fGZMUQvxHgv1vn88nOJfilzskt+3FVlxNJt9jZ663ipU3f8n3JOpxmKxe0H8W57UegCXXN+Wh5dB/Tt8zji8JlIASTWw/i8s5jsZus8Q5NOUSPoZ3x+/wh260OK6NOHxqHiJRoizRzFQshWgHU3pbUbt8JtDvo99oCuyIPL7puW/omr2yew053GcWeCt4p+JGrFj5HmbeKy354hk92/kSpt5KCmj08veErHlr9YbxDbjIMafDbxS/z5tbvKfKUU+Tex5tbv+P6xS9jSOPIO1BiKi0rjSv+ehE2p+3ANqvdQn7rXCZPUxdUk0Gkyf0TYGrt/6cCMw7afpEQwiaE6AR0AxY3LMToWFuxk+Vl2/Aav7ZW/FKn2FPBk+u/oMrvIXBQEvIYfr4uWkmRW31FPRqL9mxia3UJPiNwYJvXCLCluoTFezfHMTKlPuffdgYPfHwHI6cMoeeIblx2z/n8++dHcGY44h2aEgVHMxTyLWAckC+E2AncCzwMvCuEuBrYDpwPIKVcI4R4F1gLBIAbpJR6I8V+TNaU70CG6SFy6z5W7Cuok/T3s2gm1lfuopUjJxYhNmlrK3bi0X0h2z26n7UVOxmZ3y0OUSlHMnhCfwZP6B/vMJRGcMTkLqW8uJ67Tqrn9x8CHmpIUI2hpSMbkzAR/Mz5lU0z09KRxR5vZZ2WO4AhJS3sWREfs9xXw7e7V+Mx/IzO707H9OZHflATUB3wMGf3Gir9LobkdqZnVhta2LOwm6y4D0nwdpOlQedQUZTIJETJ31gYld+ddLMNj+7DOKgFbxYmbu4xiet/epnAQcPCzEKjrTOXXplhB/sc0fcl6/i/5W8jAB2D5375hgs6jOLGHqc19KnE1cp927lpyWtIJH5Dx6xpjG3ei7t7n8U/N3yJR/cdOLsCsGpmTmrZN54hK0pKSpmhIGbNxIsjrqN3VlsswoRVM9MhLZ/nhl9Dn+x2PDH4clras7FpZizCxJDczjwz7CqECDcA6PBcAS9/WvE2XsOPx/DjN3S8RoD3ti9k+b5t0X9yMaJLg9uXvYlL9+LWfQSkjkf3833Jehbs2cCLI6bRLaMVFs2ERZjoltGKl0ZMU6NlFCUOUqblDtDamcOro65nn6+agGHQzJ554L6heV2YccLtlHgqsJusZFmdER9n4Z6NmMJ8bnp1P58XLmNgTseI9x1P6yoK8eqBkO1u3ceMHUt4dvjV/GfMjezxVgGQb8uIdYiKotRKqeS+X441Pex2IQQtHNkN3r8ujbCD+2XtfU2VIQ3q+yJz8PNKM9nC/1KE/EYAj+4n3WyP6JuUElu6ruOqdOPMdIRMlPL7/HhdPtKynOpv2chSMrk3tpH53cImcYfJyqmtB8QhoujondUWU5hJXQ6TlSlth1DoKuOvqz5gRXlw5a8B2R24p995tHZGNtrIZwR4Yt1nfFa4FEMaNLNlcmefMxndrEeDnofSOKSUfPSvz3nz/vfxuLzYnTYu+fO5nHvLFPxeP8/e/Bqz3pyHoRvkt83jpn9fy7CJA+MddtJKmT73WMqwOPhz37MP9N8LBHbNwsRWAxiW2yXe4UXMrJn428CLsZssWLVgu8BhsjIopxPjmvfm6oXPs3zfNnRpoEuD5fu2cfXC5/DqocNMj8YDK9/n88Kl+IwAAWlQ5CnnzmX/Y23Fzmg+LSVKPn9xJq/+6W2qy2sI+AJUl9fw+j3v8OlzX/Pw5U8z6815+Dx+An6d3VtLuP/cx9i4dEu8w05aQsqYzPw/rKFDh8pkXCB7t7ucmbtX4gp4Oa5ZT/pktzvyg5qAMm813xStoNxXw9C8LgzJ7czXRSt4eM3HuA4ZCuk0Wbmrz1mc2nrgMR/jjHmP1pkUBcEROGOb9+axwZc28Fko0XZxu+vYU3hojUHIapaJq9KN31v3Q15ogrHnjeTPb98aqxCTjhDiZyll2HoRqlumEbV0ZHNZp7HxDiPqcm3pXNRxTJ1tha6ykDHuAG7dz64IZvkWeyqwaKaQ5C6B7TWq0Fwi2ltPwbGKPZU4MhwhyV0akh3rE6Y6SdJRyV2Jiu6ZrbBrFtyHzPS1a2a6ZbQ65v21S8sjYIRObtYQSfENqHBTER/96wt2bthF3+N6cvr1E8nKzzzyAyOwe1sJHz/9JdtWb6fn8K6cccOp5LaM/qzrNt1asXNDaLJu2bF52EqTJrNGr5Fq5nJjUX3uSlSMbtYj7MVWk2ZidLPux7y/dLOdCzuMxq5ZDmwTgM1k4crO4xsSatyt/G4tvx14O589P5OfZ67krb9/xNW9/0DJjj1RP9aGJZuZ1v82ZjzzFT/PXMm7j3/K1b1vYefGoqgf67rHLsfmqDunweawcv0TV3DG7ybWKVImBFgdNi6846yox6EEqeSuRMWGyl0h5RsA/IbOhsrIvnrf0H0if+g5idaOHNLMNkbmd+fVkb+lXVpeQ8ONGykl/7j6OTwuL3og+M3E5/FTta+G1/78VtSP99R1L+Cu9hDwB7u3/F4/NRUuXvjjG1E/1sgpQ7jvozvoNrgzjgwHXQd14t4P/sjoM4cx7bHLufaRS2jZsTnOTAfDJw3m6R8folXnFlGPQwlS3TJKVCzasymkfxwgYOgs3rOJ3lltj3mfQgjOaT+Cc9qPiEaICaFyb1XYFrqhGyz+cllUj+Xz+tmyYlvIdikly2aviuqx9ht6ygCGnhI63FcIwZk3nMaZNzTt8htNiWq5K1GRbrZh0ULbCmbNRLrFHoeIEpPVUX8phmiX2jWZNUyW8O03R1p0J5opiUcldyUqJrTqF3YZLgFMaNkv1uEkLEeanRGTB2O21k26NqeNs35/6Dr0DWMymTjx4jFYbJY6220OK1N+e0pUj6UkHpXclajIsabzyKBLSDPZgv/MwX+PDr6UbGtavMNLKLe9fD3dh3TG5rThzHRgsVsYe/5IzrppUtSPdcO/rqLP6B7YHFbSspxY7RaGTx7MJX8+N+rHUhKLmsSkRJXPCLCsbCsCwcDcjgdmsiqhtq4qoLhgD537t6d5+2aNeqyCdTvZtWk3Hfu0Uxcxk8jhJjGp5K4oitJEqRmqR6HYU8Fzv3zNgtJfcJisnNd+JL/pOAazZjrygxVFURKMSu5Ahc/F1B+eocLnQkdS4Xfx8qbZ/FJVxIMDLox3eIqiKMdMXVAFPt7xEzUBL/pBVdg9hp+5xWsodIUWQlIURUl0KrkDy8u34Q0zAceimfilKvrTtBVFURqb6pYBOqY1Y9GeTQRk3UJVujRo7Yh+gSVFiSWfx8f8DxdRuHE3nfq3Z9TpQzGZm8a1JCkl6xZtZNmsVaRlOxl34Wiym2XFO6wmQSV34Lz2I/lgxyIC+q/J3Sw0Oqc1p0dm6zhGpigNU7K9lJtG/wlXpRt3tQdHhp28Vjn8c8FDZOYl9hq3hmHwt9/8k0Wf/4zX7cNis/DKXf/lvo9uZ8jJTXdFs1hR3TJAG2cuTw+9ivbOfCzChEWYGNOsB/8cemW8Q1OUBnni2ufZV1yBu9oDgLvKw+6tJbx013/iHNmRff/BIhZ9/jOeGi/SkPjcPjwuL3+94An8vshW90olquVea0BOB94feyvlPhc2zYzDXH8NEEVpCgL+AMvmrMbQjUO263z33o/c9tL1cYrs6HwzfS6eGm/IdiklaxZsYOD4vnGIqulQLfdDZFudx5zYK3wu1lcUhq2KqCiJSIhwlYASy+FCbArxx5tquTeAK+DhqoUvsKW6GAgWyZrSZgh/6afqdijxZ7aYGXxSP5Z8s4KDRvmimQRjzx8Vv8CO0sQrxrNi7pqQ1rumafQZ0yNOUTUdquXeAFf88NyBxA7B98+nhT/z8qbZ8QtKUQ7SoXfokoTSgC4DOsQhmmMz5uzhjD5rODanDZNZw+a0YU+zcc/7t2Gup5Sx8itVWyZCezxVTJr797D3OUxW5p18X2wDUpRDGIbB2blX4Kp0h9zXslNz3tz8bByiOnYblmxm6cyVpOekccIFo8jMTexRPrGkass0gh2uvfXe59HVlXwl/vxef9gLkkDYBasTVY+hXegxtEu8w2hyUiq5SymZW7KWj3f8hN8IcFrrQZzWemBExcF6HWb8e24c6pcv3ruJ9wp+pNLvZnyLPpzVbhh2kxrxcyw2Ld/KB09+RtGWYgae2I+zbzyNrPzMmMfx88wVzHj2K6rLaxh73ihOu/pEbI5jXznJareS3zo37LJ+nfonfrdMMvP7/Mx84zu+/d/32BxWJl93MqNOHxrVC8Up1S3z8JqP+WLXsgMta7vJQr/s9jw99Eo0ceyXH+5e9j9mF68OPc7A33Biy9gN03p981xe3TLnwPOyaRbaOXN5ddTvsJssR3i0AvDDjJ/42yVP4fP4kYbEYrOQluXkuaWPkt86N2ZxvPnX93j30RkHWtw2p5W23Vvzrx8ewmo/9g/r797/kUenPoPX7Tuwzeaw8rcv/0T/sb2jFrdy9HRd5/aTHmDjks14XMG/sz3NxqlXncgN/7zqmPZ1uG6ZlLmgWlBdyueFS+t0mXh0P6vLd/Djno0R7fPvg37DZR2Px6aZEUCONY2/D7w4pom93Ofi5c3f1nleXsPPTlcZXxQujVkcTZmu6zw57Xm8Lh/SCDZ2/F4/VWXV/PfB92MWx76SCt7++0d1ulK8Lh+FG4v49q0FEe1z7HmjuO+jO+g5ohtZ+RkMGNeHR2fdoxJ7HC389Gc2Lt1yILEDeGq8fPHSLIq2FB/mkccmZbplfirbDGFW+XTrPn4s/YUxzSIbWnVjz9O4sWf8VnRfVV6ARZjwUXeMvcfwM69kHee0HxGnyJqO4m2lYfum9YDO4i+XxSyONQvWY7aY8XnqXrPx1Hj58ZOfOPXK8RHtd+gpAxh6ipqunygWfbEUT+2M4YNpJo3lc1ZHbaWslEnuWRYnpjBdLxZhIqcJr/GZaXEiCe1aEwjybOlxiKjpSctyout62PsycmJ3DjNy08P+LTWTRk6LYLEsd7WblfPWYraa6X9CbyzW1O52K9xUxNZV22nTtSWd+jWN6wg5LbIwWUzo/rqvOU0TUa3306DkLoS4GbiWYJP4JSnlU0KIXOAdoCOwDbhAShn3S/PHN+8Z9o0jpWRym8FxiCg6+mW3I8vixK376zw/m2bmvPYj4xhZ05GVn8mAcX1YPmc1Ad+vbzi708Z5t54eszj6HteTtCwnnmoPB18Ks1jNTPntKXz71vc8ce3zByo6aprGXz+5k77H9YpZjIki4A/w0MVPsfiLpZitZvSAQbfBnXjws7tJy3TGO7zDmnjFeD544rOQ5G4ymxh22qCoHSfiPnchRF+CiX04MACYIoToBtwFzJZSdgNm1/4cd4aUhLt4LIRAl0aYRzQNmtB4ethVtHHk4DBZSTPbsGsWbus9hd5ZbeMdXpNx939vpvuQLticVtKynFjtFs64YSInXXJ8zGIwmUw8OvMeWnRsjiPdjjPTgT3Nxo3PXoMj3c4/rgleF3BVunFVuqkur+H/Jv8dd03oV/xk97+/fchPXy7D5/HjqnTjdXnZ8NMmnr7h5XiHdkStu7TkzjdvwpER/Bs7Muzktc7h0Vn3YrVF75tYxKNlhBDnAxOllNfU/vwXwAtcDYyTUhYJIVoBc6WUh+3QjsVoma92LefhNR/j0n11tpuFiSs7n8C13SY06vEbm5SSDZW7qA546JPVThU+i9D29YXsKSyjy4AOcRkGCcG/5calW3BVuuk5oht2p43p977NWw9/HNLac2TYueWF3zL+ojFxiTVeLmh1DfuKK0K2m61mPq16s0nMYPV5/axb+AtWu5Uew7qgacfe1m6sSUyrgYeEEHmAG5gELAFaSCmLAGoTfPN6gpoGTANo3759A8I4OjUBL0aYD7KA1KkMhM7ga2qEEPTMahPvMJq89j3b0L5nfM+jEILuQ+pO2qna5wpJ7ACGblBT4YpVaAmjvslZhm6gB/QmkdytNgsDTujTaPuP+AxIKdcJIR4BZgLVwArgqMsiSilfBF6EYMs90jiO1oj8rhhh+twdJivHN2safZbf7FrBS5tnU+KpoHN6C27scSqDczsTMHRe3zKXD7Yvwq37GZ7XhZt7TqKNM3bjs5XGNXLKEL5+bQ6eQ7pgpCEZckr/OEUVP0NOGcAPHy/GMOq+pzv1ax/RhK9k1KBx7lLKV6SUg6WUY4EyYCNQXNsdQ+1tScPDbLi2zjwuaD+qzqQeh8nKsLwuDMtL/KnNH21fzINrPqSgZg9u3c+aip3cvGQ6y8q2cs/Kd5m+ZR57fdW4dC/flaxj6o/Pss9XHe+wlSgZPKEfA0/siz3t18RlT7Nx1k2TaNUpOkPnmpJpj11GWk4aVnvw/Wy2mnGk27nlxd/GObLE0aAZqkKI5lLKEiFEe+AbYBTwf8BeKeXDQoi7gFwp5R2H208sC4ct3rOJT3YuwScDTGw1gHEt+oQdIplIDGkw8du/UeEP/frdI6M1W2tKQmrJWzUzUzufwLVdT4pVmEoj03WdBR8tZs5b87HYrZx61YkMPqlfvMOKm/LSCj57/hvWLdxIx77tOfOGiTRv3yzeYcXU4frcG5rcvwfyAD9wq5Rydm0f/LtAe2A7cL6Usuxw+2mKVSFjqcLnYtKcv+OXoX2uNs2MWTNREwjtgxyR142nh6mlAhUlWTVaVUgpZcg4MSnlXkA1F4FCVxlfFy3HHfBzfPOe9MtuH1FhoDSzDYtmwh9mok0LexbFnsqQ7WZhomtG6n1dVxQlKLH7I5qwLwqXctH8p3h507e8sXUev1/yKn9d/UHYsfZHYtZMXNLpuJAiYHbNwo09TqN3Vhssom5lS4tm4oIOib/ajqIojUMl90ZQ5Xfz9zUf4zUCBGRwjI5H9zN792oW790U0T6v7nIiUzudQJrZhklo5NkyuKvPmZzQojdPDJnKSS37YREmNCHoltGSfw+7mlaOnOg+MUVRmoyUKvkbKzOLVvLQ6o9w6aH94FPaDOGeBqyxakgDj+7HYbKGdPEEDJ2A1FUdd0VJEarkb4zVN/pGILDU3ielpMi9j73eqmPatyY0nGZb2L57s2Y65sQeMHQKXWVU+ZvmRK49nkqK3eURdXcpSjJL/GlcTdDI/G5hi5TZNDOT2gxmxb4C7ln5DmXeagwkvTLb8NCAi2jhyI5pnF8WLuMf6z/DZwTQDYPjm/finn7n4jQn/iSQHTV7+b/lb7G1JjiNoqU9mwcHXKhm6SpKLdVybwROs42/DbwYu2bBYbJi1czYNDMXdRxNS0c2Ny15jSJ3OV4jgN/QWV2+g+sWv4QRwwJmS8u28Pc1H1Ppd+PR/filzvzS9dyz8t2YxRApvxHg2kUv8EtVET4jgM8IsN21h+t/epkKX+pNxVeUcFTLvZGMadaDT8fdydyStXh0H6Pze9AuLY+XNs0icMh4dQNJuc/FkrItDM/rGpP4pm/5Do9Rd1EInxFg4Z6N7PFWkW9L3BXm55esx6P7Qr4dBQyDr3Yt58KOo+MUmaIkDpXcG1GW1cmZbete6yh07cNvhI5Xl0hKwoxXbyxF7vAl9i2aib0JntxLPJVhJ3R5DT9FnrgvHaAoCUEl9xgbnNuJOcWrcet1W82GNOgTw/rrg3M7scO1N6SWvS4NOqTlxyyOSPTObotJaPg5pPytyUr/7KaxGo/ScEVbinnn0RmsX7yRDr3bceEdZ9K5f/Dvv+aHDbz3+AyKC/Yw6KR+nHfrFHJbptbQYJXcY+yUVgN4fctcit0VB1qfds3CmGY96JQetjpyo7ii8zi+KVqJK+A9UC3TbrJwTZcTE34oZd+sdgzI7sDyfQV4a7uWrJqZts5cxjZvGhU+lYbZtmYHN43+Ez63Dz2gs3VlAQs+XsyDn97F3qJ9PDnteXxuH1JCwZodfPP6HJ5f9hj5bfLiHXrMqHHucVDpd/P65jnM3r0am8nCue1GcF6HkTEvYFboKuOlTbNYUraVPGs6l3cey0ktm0YhKr8R4K1tC/hk588EpM6prQZweecTmsRIH6Xh7j71QX6euYJD01ebbq2oKK2kurymznaT2cSka0/ipmevjWGUja/RCodFS6old0VRGub0jEvDLtihmTSsdkvY+1p1bsEbm56JRXgx02iFw5TGsb1mD+sqCmlmz2RQTseIio0pTZff52fprFW4q9wMGNeHnBbZ8Q4p4aRnp4VN4CaLiUCYFasAspsfednEfSUVrJizGke6ncEn98dijd6aprGmknsC0aXB/Svf59vi1Zhru2jybBk8P/xamtnjs56nElsblmzm7lMfRA/oICHgD3D5fRdw4R1nxTu0hHLuLVN4/Z538Lp+TfBWu5VTrhhH8dYSls1ZTcD36xoH9jQb5//xzMPu871/fMLrf3kbk8WMEGAyafztyz/Rc3i3RnsejUlNYkogH25fxNySNfiMAC7dh0v3sctVxp9WvBXv0JQYCPgD/N+kh6gqq8ZV6cZV5cbn8fPmA++z9scN8Q4voZzzh8lMvHIcFpuFtCwnFpuFEVMGc/0TU7n7fzfTZ3QPrHYLzkwHVruVi+8+m+PPGVHv/tYt2sj0e9/B5/HjrnLjqnRTta+G/5v0NwL+o149NKGolnsCeX/7QjyHDJHUkayp2EmZt5pcW3qcIlNiYeW8tQS8oYnE5/bxxUuz6D2qRxyiSkyapnHj09dw+b0XsGPDLlp2ak5+6+CawVa7lce/vY+ircWUFZXTsW870jKdh93fFy/Pwuf2h2zXAzrLvl3NsIkDG+NpNCqV3BPIoWPf99PQQpK+knw8NV4Ic3lFSklNpSqrEE5WfiZZ+eG7LFt1anHU68u6Kt31Fp/zVHvCbk90qlumAaSUvFvwI5PnPMzor//MZT88w9KyrRHv78QWfUIW3QDIsabRKsZFxZTY6ze2V51+4v3saTbGnqdKKjSmseeNqrP4+H5+X4AB4/vEIaKGU8m9AV7Z/C3P/PIVpd5KAtJgQ+Uubl7yOqvLd0S0vyu7jKeZPfPAiktmYcJusnBf//PUiJkUkJGTznX/mIrNYUUzBd+a9jQbPYd3Y+x5I+McXXI77pzh9B7V40CC1zSBzWll2qOXkZmbuKU4DkeNc4+QV/dzyrcPhu1KGZ7XlWeGXRXRft0BH1/sWsrPZVtp48jl7HbDae1MrWnTqW7Tsq18+cpsqvbVcNzZwxlz1nBM5tBvdEp06brODzOW8P0HC0nPdnLa1SfRbXDneId1WGoSUyPY5drHRQueCtsXnm/L4Ivxd8chKkVRUomaxHSQjVVFfFm4nIDUGd+iDwMjnCSUa0sPmfq8X4e0Zg2MUkllOzcWMXP6XGoqahgxZShDTu6Ppqke1GQipWTlvLXM/2gRVoeVCZeOpVPf9lE9Rkol9/9s/Z4XNs7Eb+hI4OOdP3Fqq4Hc3eesY07wdpOFCzuM4t2CH+vURbdpFq7telKUI1dSxaz/zOPJ615E9+voAZ2vX5/HoJP6ct+Ht6sEnySklDx21bN8//5CvC4vQtP4+OkvuebhSzj7xklRO07KvFqKPRU8v3EmXiOAgUQi8eh+vipazsry7RHt83fdT+GKLuNIN9sRQDtnHo8M+g2DcztFN3glJdRUunjquhcPVDoE8NR4WDZ7FQs+/inO0SnRsmLuGr5/fyGeGi9SgqEb+Nw+Xr7zP+wrLo/acVKm5f5j6S9oYQYRe/UA3xavZkDOsdcB14TGVV3Gc2XncejSwKypi15K5FbMXYPJYoJD1ir31HiZ8/b8w86wVJqO797/MXzRM7OJn75azilTx0XlOCnTcjdrprBdLxoCm9awzzghhErsjWivt4rNVbvxG/GdBl5cUMr29YUYRuOsdWuxhS9SJQTYHLGvsS+lZOfGInZt3l3vBB/lyEq2l1KwbueB143VbkXTQnOREPW/BiKRMi33sc178ciaGSHbzZrGqa0Hxj4g5Yiq/R7+tOJtfi7bglloCCG4ucckzmo3LKZxFG0p5v5zH2fHhkI0k0ZalpO73ryJgeP7RvU4A8b1CduvbnPYOPXKE6N6rCPZsGQzD174BPuKKwBJs7Z5/OXd2w6sdKQcWcn2Uu4/73G2rd6BZjZhd9q4Y/rvmXDZWD574Ru8Ll+d3zd0yYjJg6N2/JRpuWdaHDw04EJsmgWnyYrDZMGqmfl991PpnH50U5SV2Lp7+f9YsnfzgUJqNQEvT6z7jJ/2bo5ZDLquc+u4e9iyqgCfx4+nxsveXfv4yxkPU7K9NKrHstosPDDjThwZDhwZdmxOK1a7hbNvnsSAcbGbJVldXsMdE+5n99YSvC4vXpePnb8Ucdv4e3HXNM2p+LFmGAZ/PPF+Ni3bFnzdVHsoL6ng/nMfw5FuZ+r9F2K1W7Cn2XCk27E5bdzz3m04MxxRiyFlWu4AY1v05ovxdzG/dD1+Q2d0fnfyVSndhFTiqWDZvm0hC2F7DD9vbvmOYXldYhLH0lmrqKlwIY263RK6X+fLV79l6n0XRvV4/Y7vxTu7XmTRZz/jqnQz+OT+tOwYu+UXAea8vQA9ENr1pPt15n+4iJMvOyGm8TRFq+evp7y0AkOvex4Dfp1Pn/+G3z4+lfEXjeGnr5ZjtVsZOWUwaVlpUY0hpZI7QIbFwWmtB8U7DOUIyrzVWDQTvjD97MWeitjFUbQvJLFDsOZIcUF0W+77OdLsjLtwTKPs+2iUFe2rUyd9P5/HT1lReewDaoLKivYhwgzg0P06JQV7AMhvk8dpVzfesOmUS+6R2FGzl9e2zGFV+Q7aOfO4ovM4+udEd8KBUlfH9GboMrT1aBYaw/Nj02oH6DWyO0aY5G5PszH4pP4xiyOWeo8O1lg5dESHxWamz+jucYqqaek5olvYOvA2p43BE2LzukmZPvdIba0u4bIfnuaLwmUU1JQyv3Q9N/z0Ct+VrIt3aEnNbrJyfbdTsGu/jh4wC400s53LO8WuW6B9zzYcf86IOhUDrXYLLTo0Y+z5o2IWRywNOqnvgcJlB7ParfQerWrKH42WHZsz4fIT6rxuLDYLea1zmHDZ2JjEoFruR/DMhq9w634kv7bevIafR9bM4PhmPVW1xkZ0cccxtHPm8ebW7yj1VjEirytXdBkX8yUH75j+e756dQ6fPvc1XrePcReO5rxbT8caxWFriWTFnDUhfcUAXreXdQs30kcl+KPyh+em0WdUD2Y88yWuag8nnDeS8247A7sztLRwY1DJ/QhW7Cuok9j3K/fXUO6vIceqVkdqTMc178lxzXvGNQZN05h0zUlMuiY1ykqsnr8+7CSbgE9n9fz1KrkfJSEEp0wdF7VJScdKdcscQbY1/BVsgcBpis0nsKLEUm7LbGxhWpcWu4XcltmxD0iJSIOSuxDiFiHEGiHEaiHEW0IIuxAiVwgxUwixsfa2SRcjv7zz2Dr9vgA2zcxprQdiMyXn13IltY27aAyaKbS70Ww2cdy5qgRCUxFxchdCtAFuAoZKKfsCJuAi4C5gtpSyGzC79ucm6/Q2Q7ik43HYNAtpJhtWzczY5r34Y6/T4x2aojSKjJx0HvnmHpq1y8OeZsPmtNGyU3Me+/ZeHGn2eIenHKWIF+uoTe4LgQFAJfAx8C/gaWCclLJICNEKmCulPGwnXVNYrKMm4KXQtZdm9kzVz66kBCkl29cXIoSgXY/WavBAAmqUxTqklIVCiMeB7QTr2H0jpfxGCNFCSllU+ztFQoiw0+uEENOAaQDt20c+ZlxKGZMXXZrZRvfM1o1+HEVJFEIIOvRqW+/9sXrvJbP9jevGOI8N6ZbJAc4EOgGtgTQhxKVH+3gp5YtSyqFSyqHNmh37ykWzilZy5rzHGPH1n5g05+98tH3xMe9DUZRjt27RRm4YficTzRdyZtblvHz3f8NO2FHqV7SlmLtPfZBTrRcx2XkJj17xDDUVNVE9RkOGQk4AtkopSwGEEB8Co4FiIUSrg7plSqIQZx1zi9fywKoPDqyAtMdbxZMbPsdAcm57dcFHURrL9vWF3DHh/gNDJV1Vbj7+1xfsLSzjzjdujHN0TUNNRQ2/H3k3VWXVSENi6AZz317A1lXb+feSR6LWim/IaJntwEghhFMEozkJWAd8Akyt/Z2pQGid3Qb69y9f11naDsCj+3lx0yxVd1pRGtG7j36Mz1P3ved1+5j33o9RXUUomX3zxjy8Ll+dmkV+X4CdG4tYs2B91I4TcXKXUi4C3geWAqtq9/Ui8DBwshBiI3By7c9Rtcu9L+z2Cp8rbKEpRVGiY/OKgrCzV612C4WbdschoqZn8/JtYQuzYUi2ryuM2nEaNENVSnkvcO8hm70EW/GNpo0jl601ob092dY0rA1cVSmR7fVWMWPnErZVl9A3uz2T2wwmzZw4E6mklCzdVMiXi4Otj9OG92Rw1zbqoluclZdW8NUr37J19XZ6Du/KKVPHRVxetuugTmxZGZrgfR4/bbq1ika4Ccld42HWm9+xev462nZvzWnXnER+69yI9tVtcCfmvvNDaIIX0L53/Rewj1XEQyGj6ViHQn5fso7/W/423oO6ZuyahVt7TeasdsMbI8S421C5i+sWvURA6viMAHbNQrrFzhujbkiYmvSPvTuXjxaswusLfnuyWc2cc1w//nj+uPgGlsK2rdnBH477M36vH5/Hj80ZXBzi2Z8epnm7/GPe385fdnH9kDvxHLRoh81p5YQLRnP7qzdEM/SEUV5awQ3D7qJybxWeGi8WmwWzxcSjs+6h5/Bux7y/6ooaLmh5DX5v3V6G/Da5/G/788fUGDrcUMgmWX7g+Oa9+Gv/C2jnzEND0MKexR29z0jaxA7wwKr3ceneA91OHsPPPl8N/974TZwjC/plZykfzV+FxxdAAhLw+AJ88P0qNhbuiXd4KevJac/jqnQd6Cf3urxU7q3ihT++EdH+2nZvzeNz7qP3qO5oJo30nDTOu+10bn3xt9EMO6G8cd+7lBXtO3AR2e/146728OgVz0a0vw2LNyHCLKdYXlrJ3qLwXc6RaLJ9GONa9mFcy9gtPRZP1QEPW6pDu6F0aSRM6eH5q7fi1/WQ7QHDYP7qLXRrc+ytRKVh/D4/6xdv4tAv54ZusPiLpRHvt8fQLvxzwUMNjK7pmP/RYgL+0Nf27q3F7CupIKd51jHvz+f2hWw3W0ws+XoFp145PuJYD9YkW+6JpsRTwZryHdQEwlwkiQKzMIVZ0yUoUa4x2K1mTGFqgJs1gd2iavDEg6ZpaFr4V44lScsVNwabPfy5khKstmN//9nTbGHr5QtNYHdaj3l/9VHJvQFcAS+3/Dydc777B79f8iqnfvs3Xt40O+rDMe0mCyPzu2MWdf9cNs3MWW2HRfVYkTp5cPewy4qBYMKQY++XVBrOZDYx5uwRmK11E5DVbmFilFqHqWDytJOxOeomXZNZo9/xvSK6MH3K1HFYrKEfCtKAEZMHRxznoVRyb4C/rvqAn/ZuxmcEqAl48Rp+3tj6Hd8UrYz6sf7S7xzap+XjNFlxmKzYNQuDcztzRZdxUT9WJJplp3P/1FOwWcw47RbS7BbsFjMPTJ1IsyxViydebn7uWjr2aYcj3Y4j3Y7NaaPXqO5c8UB0F/ZOZufddjpDThmAzWHFnm7HkWGnVZeW3PVmZJO2OvVtz7THLsNqt+DIsOPMcOBIt/PAjDtwpDuiFneTHC2TCKr9HibOeQi/EdoX1z2jFf8ZE/3ZelJKlu/bxi73PrpntqJbRuINPatye/lx7TYARvXuSIYjcYZqpiopJesW/sLOX4ro3L8DXQd1indITdK2NTv4ZclmWnRsRv+xvRs8xLe8tIKlM1disVsZdurAiFZoOtxoGZXcI1Tk3seF3z8VMlMWIN+WwRfj745DVIqipJJGqQqZ6prbs3CYrXh8dZO7hmBYXpc4RaUoqWPLygLevP89Ni7dQpvurbjsL+fR97he8Q4rYag+9wiZhMbtvc7ArlkOXEY0C400s41pXSfENTZFSXYblmzm5tF/YsHHiykuKGXpzJXcdeqDLPzs53iHljBUy70BJrTqR3N7Fm9snUehq4zBuZ24vNNYWjiy4x2aoiS1F29/A88h0/e9Lh/P3PgKIyYPViUvUMm9wfrntOfxnMviHYaipJRflmwOu31P4V48Lq9aDhCV3BXlAH9A5/vVWykpr6Zfx5b06djywH1VLg9zV2zG6w8wuk8nWufFp55PyY49LP5iGWarmdFnDiUzNyMuccRbdvMsdm8NnbVtsVmw1jPpKNWo5K4owPaScq75xzu4vH4CuoGmCQZ3a8uT15/BonXbueOlzxBCYBiSx9+bx9WnjeDaSbFdGObdxz9h+j1vIzQNIeCZ37/MXf+5iePOTr0Fai688yxeuG36gXovECxgdvr1EzGZTHGMLHGoC6qKAtz58meUVblxef34AjoeX4Clv+zkjZlLuOOlz/D4Ari9frz+AL6AzmtfLWbNttjVL9+ysoA37n0Hn8eP1+XFU+PF6/bx8KX/ompfdcziSBSTr53A+X88I1jlMsOO1W7h5MtO4Oq//SbeoSUM1XJXUl5peTVbi8owDpnz4fEHeHfuCrQwF+d8AZ3PF62r03XTmGb/73v8vtCFaDSTxsJPf+bky0+ISRyJQgjB5fdewAW3n0nJ9j3ktc4hLdMZ77ASikruSsrz60a9oyt0I3TVIQBDSnyB0NnJjSXgC9RZlm0/acikWZxaygAgEeLo+8ztThvte7ZpvKCaMNUto6S8VrkZNM8OrX9jNZuYNKJ32FLGNouZU4Z0j0V4AIw9bxS2MBUDDcNg+KToFZuKB6mXYJRdhyzuhyzuj1E2FRnYGe+wmjyV3JWUJ4Tgb1edRprdis0S/DLrtFlo3yKH6yaNICc99Ou+YRgxrVHfe1R3Tr78BGxOG0IITGYNm8PKNY9cSl6rnJjFEW1SBpBlF4LvO0AP/vMtQpadj5TueIfXpKluGUUB+nRsySd/vYrPF66lqKySQV3bcMKALqzeupsqd2idfk3T+HThWi4/OWxZj6gTQnDTs9cy4bITmP/hIqx2C+MvPo4OvaK35mZceOeCUU4wse9ngOEG95fgPCc+cSUBldwVpVZOuoNLJwyps217SXlwzcBDeP0BNsVh+cDeI7vTe2TsuoManV4AMtwiNy6kvqXeRWqUI0vK5L5tdxnvf7eSXWWVjOzVntNH9sGhVp5pkrbuLuO971awu6yK0b07MHlkbxzW2P0tu7bOI1x2t1vNMRsps5+UBnhnIz2fAzaE81yEtYmvG2zuDsIG8pCLwsKJMPeMT0xJIumS+/zVW7njxc/w6zq6IVm4roD/zl7Gf+66mAynmpLclMxbsZm7X/0CfyD4t1y0roD/fruMN++8mPQY1Ynv3aEFvdq3YHXBbny162hqQpBmtzJ5ROwqEEopkeU3gncB4AIE0vMVMu0KtIxbYhZH1FnHgKktBLYA+yusmkHLBfsp8YysyUuqC6q6YXDP9K/w+APotcPGPL4AxfuqeHOWqhbXlAR0g3vf+BqP79e/pdsXoGhvJW99uyxmcQghePrGszn/+AFkOu04rBZOHNiV/9z1m5h9wADg+wG88wkmdgh+m3BDzatNemSJEBoi93/gOBdEBog0sJ+OyHsfIaK3nmgqSqqW+7bdZXjDrFLuC+jMWrqR350xJg5RKZHYvGsPAT10jLkvoDNz6S9cO3lkzGJxWC3cdv4J3HZ+/CYKSc+3QLjRIwJ888F8UaxDihqhZSCyHoCsB+IdSlJJquTusFkx6pl0kmZXrYCmxGm3HmixH2p/i9kwJOt3FOPXDXp3aIElmWuKaOkE366H9k2bQKg1apVQSZXcW+dl0qVVPut3lmAclBjsVjMXjR8Ux8iUY9WuWTYdWuSwqXBPnbIADquZC8cNZG3Bbv7w3Ce4PL7guG9N8PerJzOqd4c4Rt14hONsZM1rhCR3JNjGxyMkJcElVZ87wOPXnU6bvCycNgtpdgtWs4kzR/Vh0nB15b2peeK3Z9AqL7PO3/Ls4/pxfN9O/PafH7KnogaX10+Nx0ely8ttL3xCaXlyFtES5o6Q+VfAFmyp1/4TOS8itLR4h6ckoKRcIFtKyfLNu9hTUUO/Tq1omaI1r5OBYUiWby5kb6WL/p1b0SIngy8Xr+eh/83C5a27fq3VbOL600cx9ZRhcYq28UmjGnw/grCAdbS66JjiUm6BbCEEg7qqYkLJYH9d9YNV1LjrvdhaVuUK2Z5MhJYO9pPjHYbSBCRlclfiw+318/rXP/HZorUATBnRmytOHRb1SUdDurcLW4bXYbUwvGdy9rkryrFKuj53JT4MQzLtyfeYPnMJRWVVFJVVMX3mEqY9+V6di9vR0K1NPhMGd8Nh/bVtYrea6d+5FaN6qeSuKKBa7kqU/LiugK27y+rUOPcFdLYWlbFwfQGje3eM6vHuu3wiY/p04qMFq/DrBqeP7M3kkb3QNFWNRFFAJXclStYW7MZ9yAVOALfPz9qC4qgnd00TTBzWg4nDekR1v4qSLFS3jBIVLXMysIcpzuawWmiZo0YrKUqsRZzchRA9hBDLD/pXKYT4gxAiVwgxUwixsfa26a4koBy1CYO7h10GzjAkJw3uFoeIFCW1RZzcpZQbpJQDpZQDgSEEKxp9BNwFzJZSdgNm1/6sJLmyKlfIAtMAupTsS/LhiYqSiKLVLXMSsFlKWQCcCUyv3T4dOCtKx1AS2OxlGwk3IU4As5dtin1AipLiopXcLwLeqv1/CyllEUDtbfNwDxBCTBNCLBFCLCktLY1SGEq8hGu1Q3C2cLSHQiqKcmQNTu4iOP/5DOC9Y3mclPJFKeVQKeXQZs2aNTQMJc7GD+iKpoW+nDRNMH5g1zhEpCipLRot99OApVLK4tqfi4UQrQBqb0uicAwlwXVokcO0ySOwWcyYNIHJpGGzmLlu8ijaN8+Od3iKknKiMc79Yn7tkgH4BJgKPFx7OyMKx1CagCsnDmfcgK7MXroRIeDEQd3o1DI33mEpSkpqUFVIIYQT2AF0llJW1G7LA94F2gPbgfOllGWH20+0q0IqiqKkgkarCimldAF5h2zbS3D0jKIoSqOR+h6k6z/gXw7mbgjn5Qhzu3iHlTBU+QFFUZocGdiO3HsOSA/gA99PSPd7kDMdYR0Q7/ASgio/oChKkyOrHgZZDfhqt/hBupCVf45nWAlFJXdFUZoe349A6IItBDYhpTvm4SQildyPUkl5NQtWb2Xr7sNeG1aSlG4YLN9cyKJ1Bbh9datfur1+Fq4rYMXmXXGdsCWNMqT3O6R/fdxiaAzSqEJ65yN9K36dBS2c9fy2CYju4jAH4tBLkN55yEDTmHGt+tyPwDAkD/5vFl8sWofVbMKvG/Tp2IKnrj+TdIct3uEpMbBuezE3Pfsxbq8fIQSGIbnn0glMHNaTzxeu46G3ZmHSNKSUpNmtPP37s+neNnYT86SUyKp/gGt6cG1VqSPNHRE5ryBM+TGLozEYNW9A1WPB54UBWh7kvArO30D1C4DnoN+2gn0SQkQ3rUlpICvvA/eHIGwg/UhLH0TOCwgtM6rHiibVcj+Ct+Ys46uf1uML6FR7fHj9AVZt3c1f/zsr3qEpMeDzB/jdvz5kb6ULl9dPjceH2+fnvjdnMm/lZh783yw8vgA1Hh8ur5/Sihqu/+cH+HX9yDuPFs8X4H4T8Nb2Q7shsBFZfmPsYmgE0rcEqh7nwPOSLtB3IvddBc5rwX4iYAORDtjBMgiReW/043C9Be4ZgA9kFeAB/0pkxd1RP1Y0qZb7Ebw1ZxkeX6DONn9AZ+7yTbh9/qivD6oklh/WFoRdjDug67zw2UICYZK4L6CzeP0OxvTpGIMIQbpeh5B+5gD4VyP13QhTy5jEEW3S9R/Ae+hWMPYiAusQ2U8hAzshsBHM7RDmRipz4ZoOHHp+/eCdizSqg4uWJyCV3I+gxu2r9z6PL6CSe5KrcnnC9qPrhqTK5UEPc5+UwftixqgIv12YwKiCJprcMfYB4a5haCArARDmtmBu27hxyKp67hC1H6qJmdxVt8wRjOzdAU2ErsvZMjeT7DR7HCJSYmlI93ZhW+d2q5lxA7viCLP6VEA3GNKtkRPOwWwnEv4iogXMnWIXR7TZTgbCvMekHyyDYheH9XjCpkqtGWiRXdOQRhlGxV8wikdglByPUfUvpKy/IRkJldyP4MazjiPDacNqNgFg0gR2q5m/XDoBESbpK8mldV4mzbNDW2a6Iblq4jC6t8nHbv31C7DDambqKUNpFuYxjUWkTwteaGT/BX4NsEPmg1G/uBhLwnkumNvza4IXwf9n/DGmXSEi4xYQWYC1dosJsCOyHoooB0jpRu45B9wfgNwHRjHUvITcNy2aYatumSNpnZfJ+/dczjtzl7N88y46tczl4vGD6KgKYqWEDTtK2BtmJSmTJvh2+SZevOV8Pl+8jq9/2kCa3cp5Y/szsleHmMYotFzI/yx44c87H8xtglPxLb1jGke0CeGAvPeQrg/B8zWYchHOSxHWsKVUGi8OUyto9iWy5j/gWwzmToi0qZH38bs/q+1yOvhanhd8y5D+1QhL32iErZL70cjLTON3Z4yJdxhKHKzfUYogtHXm8QVYsaWIc4/vz1mj+3LW6Oi8ISMltExE+nWQfl1c44g2IRyItEsg7ZL4xqHlIjJuisq+pH8poRdoa/nXgkruqcft8zN72UYKSyvo0a45x/XthNkUec+alJLFG3awfFMheVlpTBzSnQxnYl1HqHJ5+PrnX9hbUcOgrm0Y1qPdEb8KSylZvW03C9cVkG63ccrQ7uRlpkV0/Db5mYQ7nM1iomOL5F77XQa2gGcmoIF9IsLcPvYxSBksDOb7AUQGOKYEv6k0ZaYuBLuaDrnoLjQwRe9aTYNK/kaLKvl7ZDtKy7nysXdw+/y4vX6cNgutcjN59fYLyYhgMpU/oPP7pz9idcFu3F4/dqsZk6bx/M3n0qdjYoyuWL1tN9f/8wN0w8DrC2C3WejXsSVP//5sLLXXQA5lGJK/vP4Vc1dswuMPYDWbEAgemzaFMX2P/eKiYUjOfWA6O0vL64yMSbNb+fj+KyL+0Eh0RvXzUP0soBPs69Yg449oaVNjFoOUBrL8FvDOJTgk0gpCILKfRdiOi1kc0SaNMmTJ8UDdmc6IPETzBQhx9A22w5X8VRdUm4h7p39NebUbtzf4gnB5/WwvLee5T3+IaH/vf7eSVVuLDuxv/0ScO176LOxC17EmpeT2Fz+lxuPD4wsgCU7zX7m1iA/nr6r3cXNXbmbuys24fQGkBK9fx+MPcNcrX+DzB+p9XH00TfDyreczomd7zCYNs0mja5t8Xrr1/KRN7DKwGar/TTChBggmIS9UPY7UC2MXiPfr2sTuJlhHxgPSjSy/OeojS2IqUEDY1CurQNYzrDUCKrk3AS6Pj9XbdocsQu0P6Hz904aI9vnJj2vwhEl25dWehKifs6VoL5U1h05gCX4IzfhhTb2P+2zh2gMfWIdauimyxJSXmcYzN57DvH/8jlmPXMe7f76Mnu3CrvueHDzfENKqBECCJ3Yzs6XrI8L3TUvwLY1ZHNEm3Z/xazXLg5nBOy9qx1HJvQlojCGX9e8z/q12OPxzPtzpOOx9tbcB3aCkvBrvMbbkHTYLmSkxt0FAmIvI9W8/etKoQerFSBmmomPY40VyX6I73HmM3vNSyb0JcNgs9O/UKmQylcVs4rThPSPa55mj+9QZn71fToYzIdY97dQyl6wwidRiNh12ZMrpI/uEnTUsgEFd2/D2nGWcePvznHnPa4z/43M8+cE8dONoEk0KsU8kOJY73H0nR7RLabgwym9FloxAlk5Alh6PPMK3AOE8B3CEuUcD6+CI4kgEwnE6v46ZP5gOthOidhyV3JuIB66YSG6GE6fNgiYETpuFTi1zuP700RHt75zj+zGoaxscVgsmTeCwWki3W3ls2pSEmJwlhKBts6yQ7bqu07lVXphHBJ3QvzMnD+kWvEBs0rBbzditZh6dNoXZyzbyr4/nU+324vUH8PgCvPfdSv49Y0FjPpUmR5g7QfofCE6Kstb+s0HGXcEx3xGQFbfWjrzxAV4wSpHltyJ9K+p/kO3k2g8TB8EPGwcIByL7aYRoumU/hHUAOC8neH4ttbc2yPo7QsuO3nES4eKZGi1zdHz+AHOWb6ZwbwU92jZjVO+OaFrkiVhKybJNhSzfvIu8zDQmDO5Gmj1ciyL29lTUMOXPr+ALhE79H9uvE0/97qzDPn79jhIWri0g3WFlwuDuZKc7OOe+19lWvC/kdx1WC/Oe+F2DhpUmIxnYDt5ZBIdCnoIwtY5sP/puZOnJhBYBE2A7GS3nmcM/3r8avAtAywL7qVFNgPEkA5tqLxhbg8/LdOzXcRptgWwltqwWMxOH9Yja/oQQDO7WlsGxrINylErKq7GYTWGT+47SI48o6NmuechFz9KKmrC/69d1XB5fivSnHz1hbg/mqxq+I724ts58mAqPesGR47D0jdrEnkQizF2hsSpZopK7kqA6tsgJW2rXpAkGdW1z2Me6vX4+mr+K2cs3kem0c9H4gYzo2Z4e7ZqxdGPoiJmsNDsZTrXwSqMxdw4W+wphAcuwmIeTKtT3UCUhOe1Wrpg4tM5FX00I7FYLV06sPyG4fX4uf+QtnpmxgGWbCpm3cjO3PDeD175ezM1nHx9yEdluNXPLuWMT4jpDshJaBqRdSd2LoxoIOyL9mniFlfRUy11JWNMmjaRtfjavf/0TZdUuhnRty+/PGkOb/NALrft99uNaCvdW1BnD7/EFeOHzhZxzXH9euuV8nv1kARt2ltI6N5PfThkV0cxV5diI9FuQpo5Q83KwEqJlBCLj1oj78ZUjU8ldSVhCCCaP6MXkEb2O+jFzV24OWTkLwGIysWLLLsb268y/bzo3mmEqR0EIERza6Dwn3qGkDJXclaSSl+FEEyJkNq+UMuy4eUU5WsEl/dYEi3uZeyd8V55K7kpSuXDcQGYt21in9S4E5KQ76N8psjHaSmqTUkdW3AWer4KjftDB1AlyX0NoiVsZVF1QVZJKn44tuf38cditZtLsVhw2C23zs3n2pnMTvqWlJCbpejO4WAhekNXBdVMDvyDL74x3aIelWu5K0jn7uH6cOqwnawp2k+6w0aNtM5XYlci5/kNI7XUC4FuANKpjuuTfsVDJXUlKDpuFod3bxTsMJRnI8JPfQID0AImZ3FW3jKIocSWNaoyK+zGKh2EUD8GouBtpxL/s9AG28YRtB5ta1C5MHp6UPoyqJzCKR2IUD8LYdxNS39V4cR5CJXdFUeJGSgNZdgm43w0uVCGrwD0DufeChFmQQ6TfDFo2waXxACzBAmZZDx+2u0+W3wA1r4EsC7b+vd8g95yNNKK3IMfhqOSuKEr8+H4AfTt1FwcJgLGntopk/AlTC0T+l5B+I1jHgfMSRN6nCGv9M6WDRcEWUbdYmhFcScr1bmOHDKg+d0VR4imwIUxBMUC6kIF1CCbHPqYwhJaFSL8WuPboHuBfD8IUZu0bD/gPU+Y4ilTLXVGU+DG1BxGuaJsTYWrCZSHMHSDsalNWMHePSQgNSu5CiGwhxPtCiPVCiHVCiFFCiFwhxEwhxMba28Qd5a8oSnzZxoHIou7KT8GiYthPi1NQUWDuW1vO95BFRYQF4bwwJiE0tOX+T+ArKWVPYACwDrgLmC2l7AbMrv1ZURQlhBAWRN7bYB1NMMGbwDIUkfcuQnPGO7yICSEQua/VriRlAUxg7ovI/S/C1CI2MUS6EpMQIhNYAXSWB+1ECLEBGCelLBJCtALmSikPu8KEWolJUZTg6BiJCNtN03RJ6Qd0hIh+baPDrcTUkJZ7Z6AUeE0IsUwI8bIQIg1oIaUsAqi9Dbt2lBBimhBiiRBiSWlpaQPCUJTkI/W9yMAOEmEZzFgRwpp0iR1qv500QmI/koYkdzMwGHhOSjkIqOEYumCklC9KKYdKKYc2a9asAWEoSvKQeinG3kuRpScg90xGlo5FetUC3sqxa0hy3wnslFIuqv35fYLJvri2O4ba25KGhagoqUFKidw3Ffw/Az7AA0Yxct/vkIEjrzWqKAeLOLlLKXcDO4QQ+/vTTwLWAp8AU2u3TQVmNChCRUkV/pWg7wIOXRTcj3T9Nx4RKU1YQycx3Qj8VwhhBbYAVxL8wHhXCHE1sB04v4HHUJTUYOwmfHsrUDuLU1GOXoOSu5RyORDuSu1JDdlvsvP4Arz//Qq+/mkDdquF80/oz8mDu6uytKnO0hfC1lOxg3VkzMNRmjZVfiDG/AGdqx5/h227yw4s4ry2oJiff9nJ3Rerz8RUJkxtkI4zwf0Z4K7dagEtB+E4L56hKU2QKj8QY7OXbaSgZN+BxA7g9vmZ8cMadpaWxy8wJSGIzL9Cxv8Fp6hrrcH5G0T+Rwm7IISSuFTLPcZ+WLMNt9cfst1k0li2qZC2zbJjH5SSMITQEGkXQlpspqgryUsl9xjLz07HbNII6HWLCmlCkJtx+OnWUkpWbi2icE8FPdo2o0vr/MYMVUlx0qgC3wJAgPU4hJYW75AandT3gu9HEGlgG0NwrEjTpJJ7jJ09pi9vfbusTnIXgN1qZkSvDvU+bl+1m98+9T4791QgAN2QjOjZjsemnY7FbKr3cYoSCcP9BVTcFSxbiwCpQ/aTCPuJ8Q6t0RjVr0D1UyDMBN+VGuS8grAOiHNkkVF97jHWrlk2j1w7mQynjTS7FYfVQttm2bx4y/mYTfX/OR548xu27i7D7fXj8vrx+gMsWr+DV79aHMPolVQg9d1QcSfgCa4gJKsBN7L8D4m1/F0USd8KqP4X4P31OctK5L5rEmZFqGOlWu5xMLZfZ2Y9eh2/7CjFZjHTpXXeYYdBenwBFqzZFtKV4/UH+HD+Kq6bMqqxQ1ZSiedzwqwyUXvf1+C8OKbhxIJ0vwt4wtyjg28h2MbGOqQGU8k9TiwmE306tjyq3w3oOvXVj/IeNOpGUaJBGi4g3OtKB+mKdTixYVRT7weadIffnuBUt0wTkO6w0blVbsh2kyY4vl/nOESkJDNhHweEq85oAtsJMY4mNoTjNBBhBjRIP1hHxD6gKFDJvYm497JTcNosWGsvntqtZnLSHdx01nFxjkxJNsLSDxyng3Ds3wI4wHkxwtw1nqE1HtvJYBl0UILXADtk3InQsuMYWOQiXqwjmtRiHUentKKaD79fxdbdZfTv3JozRvUm3ZF89a+V+JNSgu8HpOdTQEM4zgLLsKQukSGlDt7ZSM/XIDIRzvMRlt7xDuuwDrdYh0ruiqIoTdThknuTvaDq8vj48qf1rNteQudWeZw+shcZztivdqKkhs279vDZonV4vH7GD+zKsB7tkroVGylpVCPdMyCwHsw9EI6zVOmEOGmSLffS8mouffh/VLu9uH0B7FYzVrOZ6XdcRIcWOY0YqZKK3pm3nKc++J6ArqMbEofVwrgBXXjwylNVgj+I1AuRe86tHV3iBhwgHIi89xDmdvEOLyk11hqqcfPE+/Moq3Lh9gWHa3l8AarcHv7635lxjkxJNmWVLp58/zu8/gC6EWwIuX1+5q7YzKL1qsb6wWTlAyDL+bWipRtkObLy/jhGlbqaZHKft2rLgTfaflLC8s278OuHrmKjKJH7cV1B2JnDbp+fmT//EoeIEpj3e8A4ZKMBvgUptdB3omiSyd1sCl9LRRMCTX1NVqLIajZBmNeUEMHhqMrB6jsfJtV9FQdNMrlPGdHrwHjv/cwmjXH9u2DSmuRTUhLUmD4dw7Y6bWYzU0Ym9jC5mHNMBg6tomgB++R4RJPymmQm/P2ZY+jRrhkOmwW7xYzTZqF982zu/o1ayUiJLqfdyuPTTsdhteC0WXBYzVjNJqZNHkmv9i3iHV5CERn/B+autROB7MFbc1dE5p/iHVpKapKjZaC2tvmWIjbu2kP7ZtkM7d4OTVNf/ZTGUePx8f2qLXj8AUb37kjzbDW8LxwpJfiXQGAzmDsn/cSneFOTmBRFUZJQ0g2FVBRFUQ5PJXdFUZQkpJK7oihKElLJXVEUJQmp5K4oipKEEmK0jBCiFCiIdxwNlA/siXcQCUSdj7rU+fiVOhd1NeR8dJBSNgt3R0Ik92QghFhS35CkVKTOR13qfPxKnYu6Gut8qG4ZRVGUJKSSu6IoShJSyT16Xox3AAlGnY+61Pn4lToXdTXK+VB97oqiKElItdwVRVGSkEruiqIoSUgl9wgJIbKFEO8LIdYLIdYJIUYJIXKFEDOFEBtrb5N+tW4hRA8hxPKD/lUKIf6QiudiPyHELUKINUKI1UKIt4QQ9hQ/HzfXnos1Qog/1G5LifMhhHhVCFEihFh90LZ6n7sQ4m4hxCYhxAYhxMSGHFsl98j9E/hKStkTGACsA+4CZkspuwGza39OalLKDVLKgVLKgcAQwAV8RAqeCwAhRBvgJmColLIvYAIuInXPR1/gWmA4wffJFCFEN1LnfLwOnHrItrDPXQjRm+BrpU/tY/4thAi/puhRUMk9AkKITGAs8AqAlNInpSwHzgSm1/7adOCseMQXRycBm6WUBaT2uTADDiGEGXACu0jd89ELWCildEkpA8A84GxS5HxIKb8Dyg7ZXN9zPxN4W0rplVJuBTYR/FCMiErukekMlAKvCSGWCSFeFkKkAS2klEUAtbfN4xlkHFwEvFX7/5Q8F1LKQuBxYDtQBFRIKb8hRc8HsBoYK4TIE0I4gUlAO1L3fED9z70NsOOg39tZuy0iKrlHxgwMBp6TUg4Cakjer5VHRQhhBc4A3ot3LPFU2396JtAJaA2kCSEujW9U8SOlXAc8AswEvgJWAIG4BpW4wq1HGPFYdZXcI7MT2CmlXFT78/sEk32xEKIVQO1tSZzii4fTgKVSyuLan1P1XEwAtkopS6WUfuBDYDSpez6QUr4ipRwspRxLsItiIyl8Pqj/ue8k+K1mv7YEu/QiopJ7BKSUu4EdQogetZtOAtYCnwBTa7dNBWbEIbx4uZhfu2Qgdc/FdmCkEMIpgitDn0TwYnuqng+EEM1rb9sD5xB8naTs+aD+5/4JcJEQwiaE6AR0AxZHehA1QzVCQoiBwMuAFdgCXEnww/JdoD3BN/n5UspDL6Ykndq+1B1AZyllRe22PFLwXAAIIe4HLiTY/bAMuAZIJ3XPx/dAHuAHbpVSzk6V14cQ4i1gHMGyvsXAvcDH1PPchRB/Aq4i+Nr5g5Tyy4iPrZK7oihK8lHdMoqiKElIJXdFUZQkpJK7oihKElLJXVEUJQmp5K4oipKEVHJXFEVJQiq5K4qiJKH/BwiWNCVSGXVBAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(iris_data['Chinese'], iris_data['Math'], c=labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 测试"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      sno  Chinese  Math\n",
      "0   10001       68    78\n",
      "1   10002       83    75\n",
      "2   10003       86    66\n",
      "3   10004       82    64\n",
      "4   10005       56    91\n",
      "5   10006       57    79\n",
      "6   10007       97    89\n",
      "7   10008       82    82\n",
      "8   10009       79    79\n",
      "9   10010       96    86\n",
      "10  10011       82    65\n",
      "11  10012       91    78\n",
      "12  10013       95    55\n",
      "13  10014       58    88\n",
      "14  10015       87    98\n",
      "15  10016       95    76\n",
      "16  10017       59    73\n",
      "17  10018       61    58\n",
      "18  10019       88    65\n",
      "19  10020       89    91\n",
      "20  10021       99    92\n",
      "21  10022       87    95\n",
      "22  10023       91    91\n",
      "23  10024       80    58\n",
      "24  10025       97    59\n",
      "25  10026       65    60\n",
      "26  10027       58    63\n",
      "27  10028       97    60\n",
      "28  10029       81    75\n",
      "29  10030       83    65\n",
      "30  10031       72    99\n",
      "31  10032       81    63\n",
      "32  10033       83    69\n",
      "33  10034      100    59\n",
      "34  10035       84    71\n",
      "35  10036       79    84\n",
      "36  10037       96    99\n",
      "37  10038       72    57\n",
      "38  10039       88    74\n",
      "39  10040       82    99\n",
      "40  10041       58    71\n",
      "41  10042       78    61\n",
      "42  10043       55    83\n",
      "43  10044       65    83\n",
      "44  10045       63    92\n",
      "45  10046       75    83\n",
      "46  10047       65    87\n",
      "47  10048       55    79\n",
      "48  10049       75    77\n",
      "49  10050       76    61\n"
     ]
    }
   ],
   "source": [
    "iris_data2=pd.read_csv('data/Score.csv',sep = ',')\n",
    "print(iris_data2)          #输出数据集\n",
    "del iris_data2['sno']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 0 0 0 1 1 2 2 0 2 0 0 0 1 2 0 1 3 0 2 2 2 2 3 0 3 3 0 0 0 2 0 0 0 0 2 2\n",
      " 3 0 2 1 3 1 1 1 1 1 1 1 3]\n"
     ]
    }
   ],
   "source": [
    "labels2 = km.fit_predict(iris_data2)\n",
    "print(labels2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "data={'Chinese':[25,100,90,20],'Math':[100,57,80,14]}\n",
    "df=pd.DataFrame(data)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Chinese  Math\n",
      "0       25   100\n",
      "1      100    57\n",
      "2       90    80\n",
      "3       20    14\n"
     ]
    }
   ],
   "source": [
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2 0 3 1]\n"
     ]
    }
   ],
   "source": [
    "labels3 = km.fit_predict(df)\n",
    "print(labels3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(iris_data2['Chinese'], iris_data2['Math'], c=labels2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.4323449459513866\n",
      "0.3963194727872971\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import silhouette_score\n",
    "print(silhouette_score(iris_data, labels, metric='euclidean'))\n",
    "print(silhouette_score(iris_data2, labels2, metric='euclidean'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3.保存模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda3\\lib\\subprocess.py:844: RuntimeWarning: line buffering (buffering=1) isn't supported in binary mode, the default buffer size will be used\n",
      "  self.stdout = io.open(c2pread, 'rb', bufsize)\n",
      "C:\\Anaconda3\\lib\\subprocess.py:849: RuntimeWarning: line buffering (buffering=1) isn't supported in binary mode, the default buffer size will be used\n",
      "  self.stderr = io.open(errread, 'rb', bufsize)\n"
     ]
    }
   ],
   "source": [
    "sklearn2pmml(km, \"km.pmml\", with_repr=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['km.model']"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import joblib\n",
    "joblib.dump(km, 'km.model')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "ad2bdc8ecc057115af97d19610ffacc2b4e99fae6737bb82f5d7fb13d2f2c186"
  },
  "kernelspec": {
   "display_name": "Python 3.9.7 ('base')",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
